lesson4-pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torch.autograd import Variable\n",
    "from sklearn.utils import shuffle\n",
    "from torchsample.modules import ModuleTrainer\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#path = \"data/ml-20m/\"\n",
    "path = \"data/ml-latest-small/\"\n",
    "model_path = 'data/lesson-4-models/'\n",
    "if not os.path.exists(model_path): os.mkdir(model_path)\n",
    "batch_size=64\n",
    "use_cuda=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Set up data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We're working with the movielens data, which contains one rating per row, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>671</td>\n",
       "      <td>6268</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1065579370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>671</td>\n",
       "      <td>6269</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1065149201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>671</td>\n",
       "      <td>6365</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1070940363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>671</td>\n",
       "      <td>6385</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1070979663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>671</td>\n",
       "      <td>6565</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1074784724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "99999      671     6268     2.5  1065579370\n",
       "100000     671     6269     4.0  1065149201\n",
       "100001     671     6365     4.0  1070940363\n",
       "100002     671     6385     2.5  1070979663\n",
       "100003     671     6565     3.5  1074784724"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path+'ratings.csv')\n",
    "# ratings = shuffle(ratings)\n",
    "ratings.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100004"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Just for display purposes, let's read in the movie names too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>162672</th>\n",
       "      <td>Mohenjo Daro (2016)</td>\n",
       "      <td>Adventure|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163056</th>\n",
       "      <td>Shin Godzilla (2016)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163949</th>\n",
       "      <td>The Beatles: Eight Days a Week - The Touring Y...</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164977</th>\n",
       "      <td>The Gay Desperado (1936)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164979</th>\n",
       "      <td>Women of '69, Unboxed</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     title  \\\n",
       "movieId                                                      \n",
       "162672                                 Mohenjo Daro (2016)   \n",
       "163056                                Shin Godzilla (2016)   \n",
       "163949   The Beatles: Eight Days a Week - The Touring Y...   \n",
       "164977                            The Gay Desperado (1936)   \n",
       "164979                               Women of '69, Unboxed   \n",
       "\n",
       "                                  genres  \n",
       "movieId                                   \n",
       "162672           Adventure|Drama|Romance  \n",
       "163056   Action|Adventure|Fantasy|Sci-Fi  \n",
       "163949                       Documentary  \n",
       "164977                            Comedy  \n",
       "164979                       Documentary  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_table = pd.read_csv(path+'movies.csv').set_index('movieId')\n",
    "movies_table.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We update the movie and user ids so that they are contiguous integers, which we want when using embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users = ratings.userId.unique()\n",
    "movies = ratings.movieId.unique()\n",
    "\n",
    "userid2idx = {o:i for i,o in enumerate(users)}\n",
    "movieid2idx = {o:i for i,o in enumerate(movies)}\n",
    "\n",
    "ratings.movieId = ratings.movieId.apply(lambda x: movieid2idx[x])\n",
    "ratings.userId = ratings.userId.apply(lambda x: userid2idx[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This is the number of latent factors in each embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_dimensions = 50\n",
    "num_users = ratings.userId.nunique()\n",
    "num_movies = ratings.movieId.nunique()\n",
    "num_users, num_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Randomly split into training and validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = np.random.rand(len(ratings)) < 0.8\n",
    "ratings_train = ratings[mask]\n",
    "ratings_valid = ratings[~mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "heading_collapsed": true
   },
   "source": [
    "## Dot product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The most basic model is a dot product of a movie embedding and a user embedding. Let's see how well that works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class UserMovieModule(nn.Module):\n",
    "    def __init__(self, num_users, num_movies, num_dimensions):\n",
    "        super(UserMovieModule, self).__init__()\n",
    "        self.users_embedding = nn.Embedding(num_users, num_dimensions)\n",
    "        self.movies_embedding = nn.Embedding(num_movies, num_dimensions)\n",
    "        self.user_bias = nn.Embedding(num_users, 1)\n",
    "        self.movie_bias = nn.Embedding(num_movies, 1)\n",
    "        self.init()\n",
    "\n",
    "    def forward(self, users, movies):\n",
    "        u = self.users_embedding(users)\n",
    "        m = self.movies_embedding(movies)\n",
    "        dot_product = torch.sum(torch.mul(u, m), dim=1)\n",
    "        return dot_product\n",
    "    \n",
    "    def init(self):\n",
    "        torch.nn.init.uniform(self.users_embedding.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.uniform(self.movies_embedding.weight, a=-0.05, b=0.05)\n",
    "\n",
    "def tensorl(from_int):\n",
    "    return torch.from_numpy(np.array(from_int)).long()\n",
    "\n",
    "def tensorf(from_int):\n",
    "    return torch.from_numpy(np.array(from_int)).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "model = UserMovieModule(num_users, num_movies, num_dimensions)\n",
    "if(use_cuda):\n",
    "    model.cuda()\n",
    "    criterion.cuda()\n",
    "trainer = ModuleTrainer(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/5: 1257 batches [00:09, 137.32 batches/s, loss=10.6672, val_loss=4.1231]   \n",
      "Epoch 2/5: 1257 batches [00:14, 85.19 batches/s, loss=2.3918, val_loss=1.8040]   \n",
      "Epoch 3/5: 1257 batches [00:14, 85.42 batches/s, loss=1.2616, val_loss=1.4188]   \n",
      "Epoch 4/5: 1257 batches [00:14, 85.60 batches/s, loss=0.9516, val_loss=1.2882]   \n",
      "Epoch 5/5: 1257 batches [00:14, 85.00 batches/s, loss=0.8015, val_loss=1.2280]   \n"
     ]
    }
   ],
   "source": [
    "if 1 == 1:\n",
    "    trainer.set_optimizer(optim.Adam, lr=1e-3)\n",
    "    trainer.set_loss(criterion)\n",
    "    trainer.fit([tensorl(ratings_train.userId), tensorl(ratings_train.movieId)], tensorf(ratings_train.rating), \n",
    "                batch_size=batch_size,\n",
    "                val_data=([tensorl(ratings_valid.userId), tensorl(ratings_valid.movieId)], tensorf(ratings_valid.rating)),\n",
    "                nb_epoch=5, shuffle=True)\n",
    "    trainer.save_state_dict(model_path + '/model-no-bias.pth')\n",
    "    \n",
    "model.load_state_dict(torch.load(model_path + '/model-no-bias.pth'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best benchmarks are a bit over 0.9, so this model doesn't seem to be working that well..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "##  Bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The problem is likely to be that we don't have bias terms - that is, a single bias for each user and each movie representing how positive or negative each user is, and how good each movie is. We can add that easily by simply creating an embedding with one output for each movie and each user, and adding it to our output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class UserMovieModuleWithBias(nn.Module):\n",
    "    def __init__(self, num_users, num_movies, num_dimensions):\n",
    "        super(UserMovieModuleWithBias, self).__init__()\n",
    "        self.users_embedding = nn.Embedding(num_users, num_dimensions)\n",
    "        self.movies_embedding = nn.Embedding(num_movies, num_dimensions)\n",
    "        self.user_bias = nn.Embedding(num_users, 1)\n",
    "        self.movie_bias = nn.Embedding(num_movies, 1)\n",
    "        self.init()\n",
    "\n",
    "    def forward(self, users, movies):\n",
    "        u = self.users_embedding(users)\n",
    "        bu = self.user_bias(users)\n",
    "        m = self.movies_embedding(movies)\n",
    "        bm = self.movie_bias(users)\n",
    "        dot_product = torch.sum(torch.mul(u, m), dim=1)\n",
    "        result = torch.add(dot_product, bu).add_(bm)\n",
    "        return result\n",
    "    \n",
    "    def init(self):\n",
    "        torch.nn.init.uniform(self.users_embedding.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.uniform(self.movies_embedding.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.uniform(self.user_bias.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.uniform(self.movie_bias.weight, a=-0.05, b=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "model = UserMovieModuleWithBias(num_users, num_movies, num_dimensions)\n",
    "if(use_cuda):\n",
    "    model.cuda()\n",
    "    criterion.cuda()\n",
    "trainer = ModuleTrainer(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/10: 1257 batches [00:54, 23.08 batches/s, loss=3.1330, regularizer_loss=0.5818, val_loss=1.0212]   \n",
      "Epoch 2/10: 1257 batches [01:02, 20.12 batches/s, loss=1.3774, regularizer_loss=0.5209, val_loss=0.8616]   \n",
      "Epoch 3/10: 1257 batches [01:03, 19.74 batches/s, loss=1.1836, regularizer_loss=0.4428, val_loss=0.8459]   \n",
      "Epoch 4/10: 1257 batches [01:01, 18.21 batches/s, loss=1.1640, regularizer_loss=0.4509, val_loss=0.8417]   \n",
      "Epoch 5/10: 1257 batches [00:58, 21.34 batches/s, loss=1.1606, regularizer_loss=0.4581, val_loss=0.8424]   \n",
      "Epoch 6/10: 1257 batches [00:59, 17.37 batches/s, loss=1.1437, regularizer_loss=0.4593, val_loss=0.8446]   \n",
      "Epoch 7/10: 1257 batches [01:16, 16.46 batches/s, loss=1.1364, regularizer_loss=0.4596, val_loss=0.8369]   \n",
      "Epoch 8/10: 1257 batches [01:34, 13.37 batches/s, loss=1.1233, regularizer_loss=0.4580, val_loss=0.8383]   \n",
      "Epoch 9/10: 1257 batches [01:06, 18.97 batches/s, loss=1.1191, regularizer_loss=0.4561, val_loss=0.8348]   \n",
      "Epoch 10/10: 1257 batches [01:14, 16.87 batches/s, loss=1.1153, regularizer_loss=0.4550, val_loss=0.8305]   \n"
     ]
    }
   ],
   "source": [
    "from torchsample.regularizers import L2Regularizer\n",
    "\n",
    "if 1 == 1:\n",
    "    trainer.set_optimizer(optim.Adam, lr=1e-2)\n",
    "    trainer.set_loss(criterion)\n",
    "\n",
    "    regularizers = [L2Regularizer(scale=1e-2, module_filter='*embedding*')]\n",
    "    trainer.set_regularizers(regularizers)\n",
    "    \n",
    "    trainer.fit([tensorl(ratings_train.userId), tensorl(ratings_train.movieId)], tensorf(ratings_train.rating), \n",
    "                batch_size=batch_size,\n",
    "                val_data=([tensorl(ratings_valid.userId), tensorl(ratings_valid.movieId)], tensorf(ratings_valid.rating)),\n",
    "                nb_epoch=10, shuffle=True)\n",
    "    trainer.save_state_dict(model_path + '/model-bias.pth')\n",
    "    \n",
    "model.load_state_dict(torch.load(model_path + '/model-bias.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/3: 1257 batches [01:00, 20.66 batches/s, loss=0.8232, regularizer_loss=0.2177, val_loss=0.7970]   \n",
      "Epoch 2/3: 1257 batches [01:00, 20.73 batches/s, loss=0.7299, regularizer_loss=0.1918, val_loss=0.7859]   \n",
      "Epoch 3/3: 1257 batches [01:00, 18.39 batches/s, loss=0.6926, regularizer_loss=0.2069, val_loss=0.7816]   \n"
     ]
    }
   ],
   "source": [
    "if 1 == 1:\n",
    "    trainer.set_optimizer(optim.Adam, lr=1e-3)\n",
    "    trainer.fit([tensorl(ratings_train.userId), tensorl(ratings_train.movieId)], tensorf(ratings_train.rating), \n",
    "                    batch_size=batch_size,\n",
    "                    val_data=([tensorl(ratings_valid.userId), tensorl(ratings_valid.movieId)], tensorf(ratings_valid.rating)),\n",
    "                    nb_epoch=3, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This result is quite a bit better than the best benchmarks that we could find with a quick google search - so looks like a great approach!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can use the model to generate predictions by passing a pair of ints - a user id and a movie id. For instance, this predicts that user #3 would really enjoy movie #6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 4.8061\n",
       "[torch.FloatTensor of size 1x1]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.predict([tensorl([3]), tensorl([6])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Analyze results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To make the analysis of the factors more interesting, we'll restrict it to the top 2000 most popular movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  57\n",
       "  49\n",
       "  99\n",
       "  92\n",
       " 143\n",
       "[torch.LongTensor of size 5]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=ratings.groupby('movieId')['rating'].count()\n",
    "top_movies=g.sort_values(ascending=False)[:2000]\n",
    "top_movies = torch.LongTensor(np.array(top_movies.index))\n",
    "top_movies[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "First, we'll look at the movie bias term. We create a 'model' - which in PyTorch is simply using the movie_bias field from our model. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_bias = model.movie_bias(Variable(top_movies, volatile=True))\n",
    "movie_names = movies_table['title'].to_dict()\n",
    "movie_ratings = [(b[0].data.cpu()[0], movie_names[movies[i]]) for i,b in zip(top_movies, movie_bias)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.04999130964279175, 'Waiting to Exhale (1995)'),\n",
       " (-0.04998200759291649, 'Blind Side, The  (2009)'),\n",
       " (-0.04995981603860855, 'Man with the Golden Gun, The (1974)'),\n",
       " (-0.04994472116231918, \"Dude, Where's My Car? (2000)\"),\n",
       " (-0.04993141442537308, 'Goofy Movie, A (1995)'),\n",
       " (-0.04991568624973297, 'Third Man, The (1949)'),\n",
       " (-0.049872223287820816, 'Around the World in 80 Days (1956)'),\n",
       " (-0.04981032758951187, 'Daredevil (2003)'),\n",
       " (-0.04971112683415413, 'Men of Honor (2000)'),\n",
       " (-0.04927796497941017, 'Nick of Time (1995)'),\n",
       " (-0.049206413328647614, 'Wonder Boys (2000)'),\n",
       " (-0.04917401075363159, 'King Kong (1976)'),\n",
       " (-0.048940181732177734, 'Caddyshack (1980)'),\n",
       " (-0.048712506890296936, 'Godzilla (1998)'),\n",
       " (-0.04865460470318794, 'Wild Wild West (1999)')]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "sorted(movie_ratings, key=itemgetter(0), reverse=False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2.5032389163970947, 'Legends of the Fall (1994)'),\n",
       " (2.4570648670196533, 'Mirror Has Two Faces, The (1996)'),\n",
       " (2.4322450160980225, 'Jungle Book, The (1994)'),\n",
       " (2.405385732650757, 'Total Recall (1990)'),\n",
       " (2.378448247909546, 'Fear and Loathing in Las Vegas (1998)'),\n",
       " (2.281982898712158, 'Twelve Monkeys (a.k.a. 12 Monkeys) (1995)'),\n",
       " (2.2691853046417236, 'Raising Arizona (1987)'),\n",
       " (2.2672295570373535, 'Disclosure (1994)'),\n",
       " (2.2521917819976807, 'Big (1988)'),\n",
       " (2.241617441177368, 'Casino (1995)'),\n",
       " (2.23540997505188, 'Hairspray (1988)'),\n",
       " (2.2340199947357178,\n",
       "  \"Monty Python's And Now for Something Completely Different (1971)\"),\n",
       " (2.2314419746398926, 'Fly, The (1958)'),\n",
       " (2.2290546894073486, 'Aladdin (1992)'),\n",
       " (2.2106122970581055, 'Bone Collector, The (1999)')]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "sorted(movie_ratings, key=itemgetter(0), reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can now do the same thing for the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2000, 50])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_embedding = model.movies_embedding(Variable(top_movies, volatile=True))\n",
    "movies_embedding.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "movies_pca = pca.fit(movies_embedding.data.cpu().numpy().T).components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.04307693, -0.06649297, -0.07918579, ..., -0.00661842,\n",
       "        0.0160651 ,  0.00609351])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fac0 = movies_pca[0]\n",
    "fac0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac0, top_movies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here's the 1st component. It seems to be 'critically acclaimed' or 'classic'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.081087442052062739, 'Battlefield Earth (2000)'),\n",
       " (0.076402307396108593, 'Speed 2: Cruise Control (1997)'),\n",
       " (0.071550997683035925, 'Batman & Robin (1997)'),\n",
       " (0.068964269471725403, 'Flintstones, The (1994)'),\n",
       " (0.068805214561170489, 'Anaconda (1997)'),\n",
       " (0.067166091319384252, 'Congo (1995)'),\n",
       " (0.066669380292356861, 'Wild Wild West (1999)'),\n",
       " (0.062400966418928873, 'Batman Forever (1995)'),\n",
       " (0.061999663458749195, 'Judge Dredd (1995)'),\n",
       " (0.059574785266585474, 'Striptease (1996)')]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.085105568569933454, 'Godfather, The (1972)'),\n",
       " (-0.079185794238934376, 'Shawshank Redemption, The (1994)'),\n",
       " (-0.068356316723929667, 'Usual Suspects, The (1995)'),\n",
       " (-0.068292083617868857, 'Godfather: Part II, The (1974)'),\n",
       " (-0.066492966422796576, 'Pulp Fiction (1994)'),\n",
       " (-0.066486089413128863, \"Schindler's List (1993)\"),\n",
       " (-0.063891933190106137, 'Casablanca (1942)'),\n",
       " (-0.062387024292047039, 'Star Wars: Episode IV - A New Hope (1977)'),\n",
       " (-0.060760076923112728,\n",
       "  \"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\"),\n",
       " (-0.060460652212634212, 'Fargo (1996)')]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac1 = movies_pca[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac1, top_movies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The 2nd is 'hollywood blockbuster'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.117079259900425, 'Independence Day (a.k.a. ID4) (1996)'),\n",
       " (0.11470397638518001, 'Lord of the Rings: The Two Towers, The (2002)'),\n",
       " (0.11209180913538738, 'Shrek (2001)'),\n",
       " (0.10945122645567616,\n",
       "  'Lord of the Rings: The Return of the King, The (2003)'),\n",
       " (0.10603134813111496, 'Braveheart (1995)'),\n",
       " (0.098468450292102924,\n",
       "  'Lord of the Rings: The Fellowship of the Ring, The (2001)'),\n",
       " (0.094519137682999341, 'Jurassic Park (1993)'),\n",
       " (0.093921821931929952, 'Titanic (1997)'),\n",
       " (0.087909107211966278, 'X-Men (2000)'),\n",
       " (0.087062187403512376, 'Shrek 2 (2004)')]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.072908343983229318, 'Royal Tenenbaums, The (2001)'),\n",
       " (-0.071330970118711617, 'Clockwork Orange, A (1971)'),\n",
       " (-0.068484435106144076, 'Taxi Driver (1976)'),\n",
       " (-0.067698741522599679, 'Annie Hall (1977)'),\n",
       " (-0.059576879844364584, 'Mulholland Drive (2001)'),\n",
       " (-0.05762246527767239, 'Leaving Las Vegas (1995)'),\n",
       " (-0.056248755994983121, 'Reservoir Dogs (1992)'),\n",
       " (-0.054307587626447315, 'Big Lebowski, The (1998)'),\n",
       " (-0.052210128851320731, 'American Psycho (2000)'),\n",
       " (-0.050850257636553695, 'Island of Dr. Moreau, The (1996)')]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac2 = movies_pca[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The 3rd is 'violent vs happy'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, movie_names[movies[i]]) for f,i in zip(fac2, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.1045831422783056, 'Napoleon Dynamite (2004)'),\n",
       " (0.090851112895748418, 'While You Were Sleeping (1995)'),\n",
       " (0.084702289765171634, 'Babe (1995)'),\n",
       " (0.084350225250651181, 'Star Wars: Episode II - Attack of the Clones (2002)'),\n",
       " (0.074048518053904763, 'Four Weddings and a Funeral (1994)'),\n",
       " (0.071563305191773327, 'Twister (1996)'),\n",
       " (0.070946108605455235, 'Perfect Storm, The (2000)'),\n",
       " (0.070195847526201754, 'Sleepless in Seattle (1993)'),\n",
       " (0.068504816710736188, \"Schindler's List (1993)\"),\n",
       " (0.068249257005016925, \"Romy and Michele's High School Reunion (1997)\")]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.094514880562114711, 'Fight Club (1999)'),\n",
       " (-0.094051757014773488, 'American Psycho (2000)'),\n",
       " (-0.092880567242161607,\n",
       "  'Léon: The Professional (a.k.a. The Professional) (Léon) (1994)'),\n",
       " (-0.091837364211015404, 'From Dusk Till Dawn (1996)'),\n",
       " (-0.083097582299821671, 'Starship Troopers (1997)'),\n",
       " (-0.08151110406830929, 'Matrix, The (1999)'),\n",
       " (-0.07838480475365539, 'X-Men (2000)'),\n",
       " (-0.077987460004422804, 'Army of Darkness (1993)'),\n",
       " (-0.073671304935473816, 'Guardians of the Galaxy (2014)'),\n",
       " (-0.07066425188794842, 'Twelve Monkeys (a.k.a. 12 Monkeys) (1995)')]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can draw a picture to see how various movies appear on the map of these components. This picture shows the 1st and 3rd components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJyc3QkJirLZyPHx4I7EX7PwgIiLyV/rTIwjOrSnwGLCEim0OvzSbzfsNBsNo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\nUvYKea/3n8GsYXWQvLNVV146WUGm/LShRJ+9Tt4ZbxKqV+JabChDfN9gW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lJbWIzbaM3w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cEuu/WkVQ1xDt9IIbwdjUmI79u7D1540kHo8nLz2HtZ8vR31JR97BzZH2vTry68Jl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AI6XbtZveTUp4mLep2lsw9grfJm276r0er6br/aRadtIe30G0wa8xHL553A2sqbzJyPSTn1\nEjGjXuLq+anERr1GauZrnM791FRv9+Hb+X5T362UL8Sxk88THvIwV81JxtE+ml2HbkGrbQGgubWE\nzbvm4Gg/miWzDzBv6kY0HfVs338tAy2p9nZfhKWlK9m9n0V+Ip5uc7G2MuZj0mqb8fVYyvxpm1k2\n5zDe7gvZtn8FjU3m+bpOnXkbd+dpLJt7BB+PJZ3PIgE/r+Usm3MYZ4dY9hy+vc+snuS0p4mLepOl\nsw+gsnRj274VfZ5XTx3aRk6efovJsZ+wfN5xVJYeZGS/z4mMvzI24i88/4T2JMYB5YsYB/QDuRnY\n2Cs4AEBCvEbXNYjvdCdQj3Gg/zjGgWfv5GJPASlADPDnxCSlLbAF4wA+DrgWmAV81KveXMAfmInx\nrfoNwAOD9PsfGGc9vAyEd5Yv669gYpLSBtgJ1AJTMQYB6oCfO5dXAHyCceX2NCASeALj9P+BTAUy\nEuI1LYOU6Y8F0NbrWCugxrgsYbAyQYlJyp6Rs0cBFTDYFJvDnfXOa8AoZhBcQURCK0EQBEH4/0Oh\nsGXimPeRSmTY24bh730NZWd3EDXyjygVdkilSuQyK7O3qadzP8bRLorYqNdMx6bGfcmqdW5U1R7F\nxTEOAJnUksmxnyKTde8e5GgfTW7hKsZEvARAbtFqbNXBpjq9nch4ldjIV01r+W2sA4hsziUz5yPC\nRjyAm/NkdLo2qmqTcXWaQHnlHiJCHiUrPxGAs9UHkErkOA/Q/sXUl8tUyOXWSCXyft/UB/neZJox\nEDPqJdKz36Oicg/qfmYRKORqZDIVMmlLv22NCn4YH88lAIyJeIWcgv9SU3cCN+fJQ34ePUklMkb4\n3Up2wX+IGfUiEomUlrZyisu3MG1coqmck0OM6Y0+QHT4cxSV/UR+yQ9EjXzKdNzLfQEjg+7pLPM8\nGdn/ws4m1HT9o8OeIafgP9Q1Zpit348Of840E2JK3Od8tyGAvKLvTLMwetMbOpgQ867pzTZAasZr\nxEW9jr/3CsD4HWlozOZ07kemPllZepjNlLgYo0Iew8djEQBjRr1MbuEqaurTcHUaT2b2hzg5jGVs\n5F9M5afEfcbq9V7U1B03u5ddpFI5QX63kJX/FaPDnkMikdDcWkJp+VamT/jaVM7ZcaxZHoqYUS9S\nWLqegpK1RIQ+Zjru47GYkEDjeDw6/Dkycz7AwTacQN8bAYga+TS5hV/T0Dlro0t0+AumGR5T4r7g\nuw0B5BcnDTjrRa/XMHHM+zjYGWdKGgwGTmS8xoSYt/HzWk55KZqEeM3axCTlSIwD7c96t9H5BjoI\nGGpilGMJ8Zqum5uVmKS8D5iNcUZAl58T4jX/7HGOhzC+kL698+04iUnKB4CNiUnKp3usja8CHupc\nh5+ZmKRc29n2P+klMUnpCDwI3JMQr/mq83AOsG+Aft8KNCfEa0xT7ROTlHcBNcB8jG/Z/TDOQOgK\nlAyeqd1YvvQcZfqzBbgvMUl5HcZZEp7Ac52fefQo82pikvIrjIGNMIzLDLrKlCcmKcdiDMbEJsRr\n9F0zn/rR1Ud/oGSonRQBAkEQBEEQhGFgbxuGVCIz/Wxl6UFVzeFB61TXplBetYf//tB3eVdjU45p\nQOpgN8osOADGQXNmzkfdAYLCVaaBS29t7ZU0txax/9iDHDj2kOm43qCFzrTJCrkaJ4cxlFfuwtLC\nmY6OekaOuJ/j6a/Q0lpGeeUuXJwmIJP2/8vrxdYfjINdpOn/S6VyLC1caGuvPO92erdlZekJdE95\nH+rz6C3YP4HUzNcpqfgZb/f55OT/B6XCDl/Pq0xlOrRNHD/1CsXlG2lpK8eg16LTt+HsYN6mY4/+\nWSjtkUqVZoNPlYVxen1bm/n1uzhNMP1/pcIWe9tw6hvSB7wPxnajTD+3tJbR0lbKvqP3sv/o/abj\neoPW7HsdG/W3Ads8Xz2v1UrV61nUpVB+dmf/z6I5p98AAUCI/x2cPP0mZWe34+k2m+z8r7CwcMLX\nc6mpjKajgePpr1BSvtnsWTS3mud+6/ldsbRwRiKRmT+LzqUOvb+Lrj2ehYXSHjvbkdQ1ZAx4H2Qy\nlSk4ANDSWkxb+1n2HLmLvUfu5uP/ymMSk5RNGMd6A09FML41H6rUXj+XAr2TgvTOFh+GMXlez6kp\nezvPGwZ03cCTPZP0dbY90DrqCIxr/fusux/AWGBk5/3oyQpjgATgbeCdxCTlss521yTEawbbzkVF\n37f855QQr1mXmKR8FmPA5uvONl7FOCuj6/rfBwKATRifX13nsRcAfWKS0gpYjTGgUnyOU3bd9/NK\nWicCBIIgCIIgCMNAKlH0OiIZcFp6F4NBj7fHQuKiXu/zWddAEEAu67vdb4DP9SSnPcPZ6oPIpBbU\nN54maIDlBV39mDjmPVydJvZbBsDdZRrlZ3dhaeGCm/MUFHI1zo7jKK/cSXnlbrzc5w16PRdbfyBS\n6fnf2wHb6vGcutbVd7U11OfRm406EA+XGWTnJ+LtPp+s/EQCfW8yC+ocPvEE5Wd3Ehv1GjbWQcjl\nVuw+dDv6XtPTJf1cq1TS41f8rj5zYdffRSZTmeUV6Gpv0tgPcXEc16cPl4JE2nPo0vdZ+HguZmzk\nq33qqQbIBwFgaxOMm/NUsvIT8XCdRXb+vwnyvcXsO3T4+GNUVO8nNvJVbNVByGQqdh26pc9Sgb7f\nu97Px7zPF0ouM9/5oau9KbGf4uQwhvDw4PS/vKXoSmbV78kS4jWGxCRlNsaB+lB09PrZAMh6HWse\nYltd9Qdr+9daCi/FmN/g9n4+qwJIiNd8kJik/AlYhHEt/9OJScoXEuI1A0W3qjAO4s9bQrzmb4lJ\nytcxzgaowXj//0rnrIWEeI0OeDQxSfkE4I5xecYSjPckH/DFuLvBqsQk5aoe1yhJTFJqgdkJ8Zpd\nnccdO//3vKKjIkAgCIIgCIJwCaxNKeGNLacprWvlxpAywt3Pb1Agkyox9MpO7uQQTV7x96it/Pod\njAzGSuWBh8tMcgtXIZNa4OI4YcCp3ypLN6wsPWlsymWE360DtunuMp3M7A9QKu1xd5nWeWwaxWWb\nqKpNZmzkXwft08XU7+/+XKgLbetinkdwwB3sTf4dBSU/0tCUxcyAb8w+P1u1jxF+t+LnZRznabUt\nNDbnmb2RvhiV1YdMifk6Ohqpa0gnJLBPovQBWVl6YmnhRlNT3oCBpt+Sk300hWXrUVv5I5We3xAn\nJOAO9h99gMLStTQ25xIccIfZ5xXV+wn2vx0/L+OS+w5tM41NeTg5/DrbX1bWHDI9Z01HPfUNmYSP\neHDI9a2tfLBQOtPYnEeAz3WUFNGeEK/JHkLVr4HnE5OUkb3zECQmKWWAKiFe0/vN+/nIAK5LTFKq\neswimIJxsJs5cLVBnQS0GJcgJA6h/DGMA+yKXjkVzCTEawox5kb4KDFJ+SLG7P8DBQhSgP7X4gxB\nZ2LHUoDEJOWNGJdInOxVRkfnsoDOMjsT4jX1iUnKVox5Enp6BJiAMRdDXo/jERhnEQw8HaUfIkAg\nCIIgCILwK1ubUmK2+1CLRkdmeSNrU0qGnE9Ibe1HVc0RGpvzUcjVWCgdGRl0P2fyvmDnwZuIDH0S\nSwvjoCC/OIm4qL+jUNgM2mag340cOfFHZFIlUWFPD1o2etQLHEp5BKXCHm+PBej1HVTXpdDSWkrU\nyD8CnXkE9BrjWuyQxwHjoD8182/IpMoB8w90uZj6ams/mloKqa5NwdrKB4Xcps+yiqFSW/lRXL6F\n+sbTWCidUCrshlTvYp6Hr9dy5CkPsy/5Hpwd4voM/G3VwRSUrsXbYxESiYzj6S+j1w82W/z8nMj4\nKxZKR1SW7hxPfxm5XE2Az3VDri+RSIgOf54jqU+iUNji5T7P+B2pPUZrWwWRI58EjMkQa+rTmDd1\nw6/W9/6EjXiQrPxEdh26hYjQx7FUOtHYnEte0XeMj3kbuWzgWdZ+3tdw8Pij7Eu+D1enidjbjjT7\n3E4dTEHJD3i7G3eQSDn1ZwwG7a/W95RTL6NU2GNp4ULKqT+jVNrj5x0/5PoSiZTo8Oc4mvYnFHI1\n3r5YJCYpIzFOr3dJiNcMlMju78ACYGdikvJ5jOv4G4BYjEkA/wAcvIhL+zfGDPqJiUnKVwAXjNPl\nV/XIP3BeEuI1NYlJyg+AfyQmKfWdfXYBohLiNZ8M0IdHgbWJSco/A8UY38KvAN5KiNcUJCYp38OY\niyAL444Ac4GB19sYlyE4JCYpRybEa0yBjs7dEhw625ckJim7kn6cSYjXtCQmKRXAQ8DWzuPXYRzc\nX9VjNwgPYCmwG+MyiLuBxRiTK5IQr9HQK5iQmKSsAtoS4jVmxzEmU9zRWWfIxC4GgvAriJ60mjXr\nckw/5+TVc91tm4kY/zUzF/0wSM1ua9blED1p9YA/XyqHkyuYd9WP6HQXN93tUqiuaWP8zO8orzif\nGWuCIAjD740tp822JgbQ6w28seX0kNsYFfIoUqmStVtHs3q9J00thVipPFk0YycSiZSf9y5h7dZo\nDqb8AanUAukQBsd+Xlej07XQ1l6Jv/e1g5YNCbiTybGfkFO4kh9/jmXTzlmcyf0cdWeWfejOIyCX\nW+PoYPxd2MVpPBKJbEj5Ay6mvp/XCrzdF7Bl93xWr/ckt+ibAcueS0jgXdjbjGT9tomsXu9JRdX+\nIdW7mOchl1kS5HcTmo5aQgLu7PP5uOi3UCoc2LhzBr/sW46b81RcBlnucb7GRvyVwyeeYP228TS1\nFDB78g+DDqL7MzLoHiaN+YDsgq86vyOzOZP3pdl3pKWtrE+2/0vB2sqbRTN3YjDo+HnP4s5n8TBy\nuVU/y3nMyWUqAn1uQNNRS3A/z2J89D+Ry9Rs2DGNbftW4OE6+5zBr/MxNvKvHDr+COu3TaC5taTz\nWVieVxthIx5gwph/cSbvS158XT4K2IVx54EBb37nW/2Z/8feecfXeL0B/PsmN+Nm770TiUQIkiJi\n71XUqtWiapRWh1JVpVSNGtX2h9LSUnvV3iNik4gZIrJl773v+/vjcuNKYnSP9/v53I+8533OOc97\n7k3c85xnoKwK8BZKY0Aoyg31KiDs1z2Ravx8lIkArYErwA7gFDD+t4yLshziMpRVDO4A21C649el\nQyuUJ/a7Hsr/iHLznfdQTAtY+fDeYZRlDuus/jByQHkKsA9lFYjHWYjSu+ALlOUSwx++HiXvEIE+\nKI0aV1B6QfQYOaD80BPjjEa59iEo8yS0eUZOhBo8TEI5GPj+WbJPItRV9uOfSEBAgBga+mRuDIn/\nAplZJaz+8TanQpJISSvCQF8LZ0dDenZzoX8fd/T1Xszt70Vp3HILM6e9RL/eylwn73wYQl5BOfNm\ntUBPV4aZ2bP/yO/aG82cBVe4dn5wrdd/FP2HH2LYIE+V7ukZxSxYepXbd7OJTyigT09XFs5pqdan\nokLBqrW3+GV/DGnpxbg6GzHl3aa0CbJTyRQWVfD1iuscO5lIVk4pPl6mfDI1gEYNLFQyoijy7aob\nbNt5n7yCcvx8zZn1cTPquZuoZOYvCaOgoJx5n/1+X4okJCQk/mhcpx2gtm9YAhC7oOefrY6EhIrk\ntBMcPdOdIb3T0dE2eXYHiT+MB6lHOH72ZYb1zUZLZvC7jSsIQpgoigG/24ASNfhph3ZT4ADgMXJA\n+d/uJOunHdr9UXpvNH0iAeQzkTwIJP7xPEgupO+Qg4ScT+bdCX7s3tSD7T93Z/xoXy5cTuXk6Wcl\n+Pz9iU8swL+xJQ52Bs9lHPiruHotg5i4fHp0cVa1lVcoMDXRYdyoBvj5mtfab9mKa2zecY8ZUwM4\nuPNlhgzwZOLk00TczVbJfDLnImcuJLPw80D2b+tFUKAtI8efIDW9umTs9z9F8OPPd/j0o5fYuaE7\n5ma6jBp/gsKi6lw1/fu4s/dQLLl5v59bpYSEhMQfjZ1J7SexdbVLSEhISPxzGDmg/CrwCcoSgn9H\ndIDRL2ocgN+Yg0AQBDNgK8qFiQMGiaKYU4vcCKprPM4VRXGdIAh6wHaUbhNVwD5RFKc9lB8JLKK6\nXuP/RFGsUb9TQgJg1heX0dAQ2LWxB3ry6o+0o70B7ds48LiXTHJKEXMXhXLhUgoALVvY8unUAGys\nq7M9b9lxjx/WR5CSUoytrR5jRjbg1X71VPfjEwr4ZM4Frt3MxN5Wn2kfqCeo8WyyAYC793JYvvom\nb49ryKTxfqSmF7NgSRhnLyjnbuJnwScfBuDibPRcz5mQWMD8JWFcv5VJUVEFri5GvPuWH+3bOKhk\njpxI4H+rbhCXUICujiaeHiZ8/WVrLMxr/0K673Asgc1s0NWtXjcHOwM+/UjpNnf4eEKt/fbsj2Xs\nKF/at1bOPXSQIecvpbD25wgWf9GK0tJKjp5I4NtFbWgeoPT4mjTej1MhSWzefo/3JzZGFEXWbbrD\n2FEN6NpJmSRp4ZyWBHbcwf5DsQwe4KlcTw8TrCz1OHoigUGPvQ8SEhISf2emdPVSy0EAINfSZErX\nuip3SUhISEj8kxg5oHztX61DXYwcUL7p1/b9rR4E04AToijWQ5msoUa2m4dGhFko6zs2A2YJgvCo\nQOliURTrA02AIEEQuj/Wdasoio0fviTjgESt5OSWcfZCMsMGeaoZBx7nUUkehULkrfeDycoqYf3q\nzqxf3Zn0jBImvH9aZUQ4ejKBOQuvMGKoN/t39OL1IfWZPf+yygtBoRCZOPk0CgVsW9eNeZ8F8u13\nNyh/7AvguWP9cXUx4o3XvDl3rD+jX/ehpKSS18ccQ0dHkw0/dGbruq5YWsgZOf4EJSXPl+SmqLiS\nNkF2/LiyI3u39qJrRyfenhxCdKwyfCojs4QPpp2lby83Du16mY1rutCn59MrsIReTaehj9lTZWqj\nvKIKHW0NgqN+5vUNyhK4urqahIUrq6hUVolUVYno6KhXv9HR0SQsXFmvODGpkIzMUoICbVX3dXVl\nBDS14ur1TLV+jRqYc/lq+gvpuOLMWBYe7//Cz/ZXsD38CybvrvYE/DN0f5E50gviefUnfaIzr77w\nPIVlOYzd4kJq/vwhPcYAACAASURBVB8ff/prWHpqOPtuff1XqyHxL6RvE3vm92uIvYkcAbA3kTO/\nX8PnTlAoIfFHYWfdkZEDyqXwgr8BDjZdGTmg/HcNL5CQ+K381ioGfYB2D39eBwQDHz0h0xU4Jopi\nNoAgCMeAbqIobkaZpAJRFMsFQbgKOCAh8QIkJBYgiuDqon4K37rrLgoKlAk7e/dwZc6M5ly4nEpk\nVC7H9/XBwU75h3jJvCA6997D+UupBLWwZe36O/Tp6cZrg5UnPK7ORty+k83qn27Toa0D5y+lcD8m\nj5P7+2Jnq/Q6mD4lgKFvHFXNbWkhR6YpoKcnw9JCeXK/Y/d9RBEWzA5UGSw+n9GcwI47OHUmSc3F\nvy68vUzx9jJVXb/1ZkNOhiRx5HgCE8Y0JD2jmIpKBd06OWH/8Pk8PWr/z3/FmbGcjt6IXg/lL23Y\nZnPqWb7E8IB52Js8+3SrVaAdP226y9dNOvP1Kzc5dzGFoycTqapSGloM9LVo0siCFT/cpJ6HCZbm\nuuw/HMe1G5k4Oyp1y8wsBcDCTN27wcJcTtpjYQgAJta53DEbTHTmGVo0ncKalTPp3bMtAJZOHdWu\n/0i2bD/CtlM/otVsH3ItQ1a9GoOOrLoW8YPcu0zerfQo+X5wPEa6FnUN9btTVlZO87Yj+GnVZzT2\ne/Z7OLL5Iv6MHDS7byyisUNXbIyqS6n9dOlDItMvkpgTgYncmv8NrFl950LsTn65sYiU/PsY6VrQ\n1XscvX3fV5M5cmcVh++uIqMwHgt9R15pNIW2HtX5gioVFey+sZiQ6I1kFyVja1yPYf6f09ihuq76\ngMbT+OxQNzp6jkRP+/mylktIPC99m9hLBgEJCQkJiX8Uv9WDwFoUxZSHP6eizFD5JPbA42UsHjxs\nUyEIggnKcg4nHmvuLwjCDUEQdgiC4Pgb9ZT4j7FpbRf2bOlJI19zysqVp/vRMXlYWcpVxgEAJwdD\nrCz1iI5RnsJHx+bR1M9SbSz/xpaP3c/H2kquMg4A+PlaoKEhPFWfW3eyeZBcSJOgrTRuuYXGLbfg\n33obefnlJCTWWZJVjeKSSr5cdpXu/fYR0GYbjVtu4VZEFsmpyrwo9T1Nadnchl4D9/P25NNs2naP\n7OzSOsdraNueuz8uZoBFMNO77KG8qoTFp4Y8ly4zpgTg6mxEn0EnaNn2OHMWXKZfb3e1dVg0NwgN\nQaBN1134Nt/M+s2R9OrmjPCMtcrMTGbf/vn0eGWSqk1Hp25b5q3Q7XTt9McnMCwvr2DeorW82r8r\nAHraxvxyYTWjxn1GQNBwLJ068uXmqVjoq/+5Kiws5pPPltMkcAiO9brT45V3CL+uXvp30bJ13I2M\nw9KpI5ZOHdmy4whHT1xk6ozqk+30jGze/mAhvgGDcPLswaDXphEdW51fQ0dHm4njBjFn/vMlq9XT\nNkZf5489PSqrLOZk1Do61Buh1q4QRdp4DKONR+11s8MfHOGbkFF09HqDxX2vMLrFMg7e/h+H73yn\nkjl693s2hn3KAL9pLOkbysDGn7D24geEJR5UyWy9Optjkd8zstlilrwSRmevN1l8agixWdWJgJ1M\nfbE2dOFM9B9fMURCQkJCQkJC4u/OMz0IBEE4Tu1lIz55/EIURVEQhBc+jhIEQQZsBr4RRfGRD+o+\nYLMoimWCIIxD6Z3QoY7+Y4GxAE5OTi86vcQ/lN3hSSw6EklyehFawP7zD+jSofr9d7RXGgEej61/\nKk/fsyI84/6zEBUi3p6mfLWgdY17xsZPLwH1iIVLwzhzPpmP3vfH2ckQua6MqZ+eo6JCmXtEU1OD\nH1d25NqNTM5eTGHH7vss+TacDT90UfM8eISWpg6GOlaUFxvhZu5FD593+PLEAMorS9CWKU/1FVqZ\npFv9xBublBsqT6vmjGj2JbZmHqz8qh3H76xnXehk1g9PZ/E34bi3P8Tk3Uvo1+gjtobPRtY+gzcG\nt2WQzxLc7B1596MzONjLWXd5KqfubaDhuAo2X7uMcZxAUm4ks7of5vqNi3jXb8bde7e4FxWPZz1n\n8gvKQV9d/4ScW6y7/BGR6RfR1pQT4NiDkc0X1TgFPhixnL23vqK8soQAp16MbvGV6tR/9qFu2JvU\nR1/bmBP3fkQQBNq4D2VYwBdoCOr2030HQ9DV1cHXx51didDOYziXo3fi5DiMXt1bM3/xGrJ0TtLH\n4212Xp+v6vfu1EWkmm4n4O1Siquy0ay4w8RlIWyfdxR7W6VN9a0xg7ieeoC1odsB+DZ4NBfCL9On\nVzsA4rJuMGVnByp0PVn/w1qMDPVZtH0qH+5tgty0Agt9R/o0/IABr/Tnsy9WMXffQOQGWkxuXx1+\nphAVvL29Pj0avE2vBpNYcWYsBWVZfNRpJ6CsKLH/9jccj1xDZlEiRroWtHYfwlD/OaoxMgsT2Bw2\nk8j0i1gaODOy+Zc0sutY52c2/MERQMDLSt2A80aLJco1vbWMG0knavQ7E70Zf8cedK0/FgBrQ1f6\nNPqQvTeX0rX+OARB4Ez0Zjp6jiTIbZBKJjorjD03l+Lv2EM1Tp+Gk2nqqKxb3aX+GG6mnGL/7W94\np0112KC/Y0/OxW6nq/e4Op9FQkJCQkJCQuK/wDM9CERR7CSKom8trz1AmiAItgAP/60tSDgJePxI\nzYHq5IMAq4EoURSXPTZnliiKj1KW/wCoZ4FT12+1KIoBoigGWFpa1iUm8S9id3gSH++6SVJuCaK2\nBgpzGYf3x7H1QvxT+7m7GZOeUcKD5EJVW8KDAtIzivFwU24q3V2NuXo9Q61f2LUM3FX3jUhLLyEl\ntbqayY3bmSgUT7eN+XibEZ9YgKmpDs5OhmovE+Nn161+pEefXm507eREfU9TbKz1SHhQqCYjCAJN\n/Cx5Z1wjdm7sjpWlHgePxtWtV30z7sfkUVJRwIXYHTiZNlAZB8oqiymqNx1B1GJWt8N83uMkJnIb\n5h7pRVmlMgRAJhMQBIHKSpEjJxJwdTEiozCBC3E7mdx+M5902Uti7g2Ox39JXn4ZZ88n4xh4ktP3\nNzK+1XIyj84kKaWAszHbAMjNKyYu/hZ9e3Xm5R6t2bhVWRY2Nj5fTe9KsZR5R/ugKzPg7NeeBMqn\ncS/jEivPvUVKagZjJn7Ozj0nuRJ9jHW71zHCZw3vtl3Hlfh9TFs9mtadRvPL3pOEXbvDoRtr2Xfg\nDB8E7eaN5ks5GLGcC7E7aqzVzt0n6NKxheq6ldtgUksieGtSF/r37YhpvSw0RB18bKqNQCWlZRw8\nEkJQk5Z81HUrX70SzlsdlmAfGM/SHTNUcgb6cjRlmlhbmWFtZcaD5HT09HQJauHHnbRzzDrUlehT\nVswd/jNNG9cnWxZOge1BMq4601pzEd19JrDmwnvEFJylWUADCmOcCE88THF5nmqOiNQz5JSkEuRa\ne93zzVdnsev6Qvo2msySvqG8324D5vrqUV9brs6mu88EvuxzEXeLpnx9eiSlFYW1jgdwN+0cbuaN\nVWE1z0uFohytJ+qHa2vKySpOIqMw4aFMGVpP1IjW1pRzPzOUSkXFY+M8KaNLZNoFtTYPC3/uZ4ZS\nXlnyQnpKSEhISEhISPzb+K0hBnuBR76jI4A9tcgcAboIgmD6MDlhl4dtCIIwFzAG3nu8wyOjw0N6\nAzUDVCX+syw6EqmWFbrSRw9RFPnso/PsPxTL/ehcYuPz2X8olrv3ctB86NLesrkNXvVM+HD6OW7e\nzuLm7Sw+nH6OBvXNCGymdJIZPcKHPQdi2LA1krj4fNZvvsu+Q7GMGdHg4Ri2uLkYMfXT80REZhN+\nPYN5i8OQyZ6+Aerd3RULc13eei+Yy6FpJCYVciUsjflLwoh7YvNbFy5Ohhw/lcjtO1lERuXw4Sfn\nVOETANduZLDi+5vcuJ1JckoRJ4IfkJpWpDJ+PMm1pGMUNh3IbYtujNxoQ0TaWd5p8yMAEZHZ7Ljw\nEwpRROv+JIoy7CnJsmZs4LeUVhax69w2jpxIICunFIVCZPTbJ1EowL+xFQpFJRNarSLhrjkp9xwI\nsBlGaOxxXhtzHDdXY5I0ttLb931auLzCsJc7EPxjV7REM4pLKnl9zFq0tQ0ZO7oVg/p1ZtvOY+Tn\nlxJ1P1dN97jSYEori3m7zQ8Upethrd2QMYHfcjl+DwPeHIeOjjYd272EqaElNrkDmDDmO+qZtaKF\nyysUaEWS8CCV3fuCqe/pgqOJN3cOWbNmRTCBrv1pYNOGmynBNdbrUugtGjfyVF0b6JgS4NiT4Pvr\nAdBzj8GiLBDhMXeUqsoqKiugheUYPCz8sTJ0JtC1P5Xx3sSXna71fSksKiEhMRUHO2vCEg+x8Hh/\nujl/ROIFW3R1lN4m+25/TWv3IRTc9eB2WDbdvd+ildur7Lm5lCZ+9bl1phw9bSMuxu1WjXs2Ziu+\ntm0x1bOtMWdpRSEHb/+Pof5zaF9vBDZG7nhaNVed4D+iR4O38Xfsga2RB0OazqawLJu47Bu1PgdA\nRmFirfM9Cz+7ToQmHOB60nEUooLkvCj23/4GgNySVJVMcNR67meGIYoi0ZlXOXnvJ6oUFRSUZj6U\n6cjBiOUk591DISq4kXyCy/F7yXk4xiNM9WypUlSQXZyChISEhISEhMR/md+apHABsE0QhNFAPDAI\nQBCEAGC8KIpviqKYLQjC58CVh33mPGxzQBmmcBe4+vCE6VE5w0mCIPQGKoFsYORv1FPiX0Ry7hOn\nfHqaVLQ0QhFTyrKV10lJLUZLpoGbqxFDB3oy/GHCQUEQWPlVOz7/8gqvjz0GQGBzW2Z+FKA64ezc\n3pFPp77Emp8jmLc4FDtbfWZ93IwObZUnqRoaAsuXtmXGnIsMfO0wdjb6TPugKZOnn3uqznK5jI1r\nurD4m3AmTQ2hoLACa0s5zV+ywcjo+UIMPp7sz/TZFxn6xlGMjHQYOay+moHAwECbsGsZ/LwlkvyC\ncmxt9JgwpiF9errVOp63dRBDGi1l8KgjLF7YmDuFm5l3tDdze52m7+BgHNrtx6JhGlkNBjDztDLM\nQi6XUV5ZTGZxLOu/v06BwTVsW1fhYiln0ectCUm6jYWBE3raxhQU5rLk23BKTVOxa52OfxNLxo91\nZdLeNDwslRn7x4z0obSskqNRdqRoZ3IvMpQ3R/bEQF+Lli38kMt1WPLNPqws9dR0z6tKwNnUF7mW\noarNy6oFoIGWcT7fLpnKyrPjcNFuwLT5k/Fu0p+jxy9i6mBLpVBAVZUV3y6ZylfnLuNh3AinoS3Y\nvO0woNws5peqe5Hk5RWSn1+EjbU5UJ0zon2911l1bgKdvN5ExzYV8/wWav0MDPR4yd+Hb/Z8wsGs\nfHLLkikuK0LmWkZV0RMxEw/ZtfsECoWCSr0UlpwczDttfyTAvjdf2l9i7pdr+GrhByTl3kWR6EVy\nSgZp6dnK57duSWjiQVpYm5OYmEF/1wGcjdlCB88RVFSVcTl+DyOafVnrnA9y71KhKMPXrl2t9x/h\nbOqr+vnRxj/vibV6nPKqEow1rZ46Zm109BxFWkEMi06+SpWiArmWEd19JrDj2hcID0M/+vtNI7ck\njZkHOiAiYiy3oq3HMPbe+kolM7L5Ilade5sPdvsjIGBt6Ea7eq9xKmq92nyPvGbKqyQPAgkJCQkJ\nCYn/Nr/JQCCKYhZQIwBVFMVQ4M3HrtcCa5+QeUAdkd+iKH4MfPxbdJP492JnIifpSSOBjgY2geYc\nn1Zrqorqvrb6rPyq3VNlhgz0ZMhAzzrvuzobsXFNF7W2a+cHq10f2PFyjX4W5nIWzG5Z57j9ervT\nr7d7ndf2dgasW9VJrc/o131UP3u4GbNm+dOf/3F0ZHrUs/NmcI9Sju0uZe7MFYzcZMuJyLXcC5/J\nDxeuEJ2Zz7tt19Xoa6BjyjvdzQiOymXtpe0smhukvJEEmhrKPys9ujjTo4szwVEFrL20nVnTmqm5\nvIPSaDNpvB8aIU48yKokND+J8aN7qu7179uRdRsPM/uLUexWr3xYO6JIRmYOLt69cO95B5m8gtWT\nelNSUkZcfDL2DgIiIg721hgZKfNUyDS0sLE2JzNL6aUgICCKCrVhS0qVEU86OurGnIZ2HRAEDZaf\nGUNZqjXa8pq5HsZ+3IhNN9dxfJMjhUn2eDi7Y9U0iSK9W7U+ws+bD2Lf1gobI31KK60IjvqZAMee\n/LRqNu9OXYxno1do/XEhhbGJdGzfTK0SgSAIyHV1KC0to7XbYGYcbE92UTJRmVeoVJTT3LnPcyxi\n3WhqaKnNBdRYq8cx1DWnqDy3zvt1IQgCwwLmMqTpbHJL0jDSteBmyikArA1dAOWm/q1W3zGm5bfk\nlaRjKrfh+L21yLUMMdJVhpsZ6VoypeNWyitLKSzLxlTPlk1hn2JtqF7+s7As56H8n1d1QkJCQkJC\nQkLi78hvDTGQkPjTmdLVC7mWplqbXEuTKV2fXdrtv8zu8CSCFpzEddoBDt5KITVPWeHgrdENcHQw\noEohoiFoUFalzC/gat6Y1PwYDHXMsTFyV3sZ6Jj9Kh30tI0xkVsTnRmmanvkHp6WnkVVlYLGgUOw\nce2MjWtnvlmxhdzceBrUVy+FaKzpRELObUoqqk/zI9MvgiBib1yfU4dX07VTS1o0a8Spw6u5eHod\nI4b3UslqydRto4IACkXdG10zUyMEQSA3Tz3eXkPQoK3HcCJSQyiOrt1TI1txFx+7QM6tD+Xs7v0c\n3rKBSq1sZDLNGrI3b9/n2o1I3F0dMNAx5dOuB8guTmbxycH4NHAm+PBqom/twcuuMe1725OTk4+z\nk/IkPzLtPA7G9cnJzcfc3AQPywBsDN05F7uNszFbCXDsia5W7XWW7U280NLQ4VZycJ1r8GtwNfPj\nQe6vjxDT0NDETN8OmaY252K342nZXLX5f4RMQwtzfXs0NDQ5H7uDpg7daiSY1JbpYqZvR5VYyaX4\nPQQ49lS7n5gTgZmeHSby2grxSEhISEhISEj8d5AMBBL/OPo2sWd+v4bYm8gRAHsTOfP7NZRqTT8F\ntcSOQHF5FZFp2Wy+fI1KjWxeHqDF+tAplFYU4u+gzADfyu1VjOVWLDr5KhGpZ0gviCMi9Szrr0wj\nJf/+r9alu/cE9t5axuX4vay/cIaeK4aRkJNIcmoW/V4byKnDq1Wv4COraeDjzr6DIWpjuOi2Q0cm\nZ/mZMehbFZNWfpPvL0zCRmhGdEQB5qbGGBrooa+ni5uLPW4u9piaGP1qnbW1tfCq58y9qJqJMPv5\nfcT3g+MpTXCopSfYGnsQm3WNe9khiPIcNl6cTZleLHJd3RqyP286gLOjLdbW5oDyRFtpJEhiyakh\nVFSVYWRkQL/GHxISvZl0ndMEtnfm0J2VnI3ZSu+G73M3Mo5GvvUA5Xt48t46whMP09p9cI35HiHX\nMqS7zwQ2X53Fqaj1pObHcD8jlKN3n69kYl342XciKS+SgtIstfbU/Gjisq6TU5xCpaKcuKzrxGVd\np7KqHID80kyO3v2eB7l3icu6zk+XPuRi3C9qIRLJeVGERG8iJf8+9zNCWRY8gsTcCAY3na2Sicq4\nwqX4PaQVxHIn7Rzzj/VBFBX09n1fTZ+7aefws1f3zpGQkJCQkJCQ+C/yW3MQSEj8JfRtYi8ZBF6A\nJxM7Ahjq3WB3RBC7I5QbRDtjT95vt4EGtm0AZQjCZ92PsClsJl8FD6e4PB9TPVsa2LRBX9vkV+vy\nsu975Jak8c3psZRUKCgsa0dxnjey8hiCy63pXqyt9t6+8nI7Nu7distg0BSUp+4yQZfpXfaw7vJU\nAsbdJCT/cwLd+jCo41wu/TiF1978lKZDchC1yjh/6QaHj55jxPCaYR8vQvu2AVy6cpN2vaurvpaX\nVxD50GhQWlZBekY2MXHqYQiyND+cdFrzdfBIqhQK8u5bI5Q2Qv8l9U2zqBDZsfsEb49/FYHqBIZK\nI8FBpmxvy4xdvRgTsJrMKDNSQ3zw6pjA9gcjsMh1YnTgMvwdezDmygamTR4FQGv3wWy/NhdjXUsa\n2T19AzzEfw762ibsur6QrOJJmOha0cZj6G9aMydTXzwsAjgfu0OthOCqcxOJSDujuv5onzL05tv+\nEVgZOgMQEr2JDaGfACL1LJsxq9thVe4KAIVYxYHb35KcF4WmhhYNbNrweY8Tqv4AFVWlbL06h/SC\nWHS1DGjs0IWJrdegr1P9+S2vLOVywj6md64tx66EhISEhISExH8L4fH41X86AQEBYmho6F+thoTE\n3w7XaQeo7TddAGIX9Kzlzh9P0IKTqlwSPhbjyEs2J0l7HvYmcs49lksiLj6ZjkP7EjDmNqtejaGe\n11DWrJxJ755tAbB06qh2nZ6RzecLfuD4yUvkFygTCwYFNmbW9LGYmxnz5dJ17DsYwpnja1RzbN5+\nmGmffkv83QN16ht1P4H23cdy88o2lTdCQmIq/kHDasi2bOHHnm1LAdi9L5gvFv5AcmomJsaG9OrR\nmk+mvKHKgfCITdsO88FHS7h2YTM2NjVj4Vev3cXyVdvIyMzB2sqMQf27MHnScLS1q/MCXAm7zZAR\n07kZug257vOVz/yjufbgKD9dnsrSvmFoaNQMq/irOXJnFaGJ+/mky76/WhUJCQkJCYnfBUEQwkRR\nDHi2pIRETSQDgYTEf4DHN+OP8+Rm/M8gozCB60nHeWdjFVCJlckJrE0Pczt+HkWl7mpGi8qqcjKK\nEthw5RPSC2NZ1Ofyn6rrk4yZ+Dn1PV2Y/O5rf6kedfHG+Nk09PXg/bdrGi3+Sg5FrCDAqReWBk5/\ntSo1OB65Fh+bVtgZ152YVEJCQkJC4p+EZCCQ+C1IOQgkJP4D/J0SOwqCBiHRm/B1mU4D5xkYyKOI\nTJxOUamyYoOdSXVCwsj0i3y0N5CCsizebr2mriH/NGZNH4uBgd6zBf8CysrKaeDtxvg3B/zVqtSg\nu8+Ev6VxAKCT1xuScUBCQkJCQkJC4iGSB4GExH+E3eFJLDoSSXJuCXYmcqZ09fpL8zg8Spz4eG4E\nuZamlHBSQkJCQkJCQuI3IHkQSPwWpCSFEhL/Ef5uiR0f6fJ3MlpISEhISEhISEhI/JeRQgwkJJ6D\nkO+OsHrAor9k7g1vruDIgl0v1Gdek8ncOXb9D9Lo95u/bxN7zk3rQOyCnpyb1uG5jAPxodF812c+\niipFnTJHV/fg8p7Jz339e5EelcI3XWZTXlL2u4/9OL8s9CUi5Js/dI7fgz9qnZ+XDR8bEX9z9182\nP0B02Ea2zLL9S3X4L3B0VTdir237q9WolcSIAxz8tg3/Jo9NCQkJCYl/L5IHgcQ/nn0zN3NznzK0\nREOmga6hHhbu1tTv1Igm/QLR1PprMqdveHMFlh42dJ3WT9UWvusiR+bvostHr9B0QOBzjdN/yUg0\nZL+vLS83OZsVPb9g1Ib3sG3g+Nz9lveYS15KTp33nfzdGf7DhN9DRe5dWkvYgekMmpmApkxZOrCq\nspztU+Zg1SAbDU3lmtw9cYMrm06QdDMKDU0DrDzseWn4TDzbNVAbLy0CVq37ktwHmWgZuOHZBehT\nfV8URc6sOsq1nRcpLSjGzteZrh/3w9K9uqxhVnwGp5btI/rCdRA1sfZypvW4rrgH1ScmfAvnt43F\n0PZdLv8cQquxnQG4dnQOMVe30G9axO+yLn8kBVkxXNn7IRnxlxAETcwdmxLYfwV6xnZP7Zcac4bj\n3/dkwIxYdPXN/xRdSwrSuRW8mKS7hynOS0ZH3xxTmwZ4BY7Dvn7XP0WHP4pfFvriFTgWnzaTftM4\nhTnx7P6yYY12B5+etHtt828aG0BRVcmmGWa0Hb4Jxwa9fvN4v5bEiAOUFKbj0qg6/8a9iz8Qd2MX\nOck3qCjL55Vpd9A3VjdAZj24SvjhWWQlXUNDQxMn37749/wCmbY+AFFX1nFp1zu1ztnjnTOY2fkB\ncHnPZDITQ8lNvY2esT19p6gbRx19enLj+Dziru/AtfHA3/PRJSQkJCQkfnckA4HEvwKX5vXoPXco\nokKkOKeQuMv3OfPdEW4dCGPoqvFoy399ybeqx2Lkfwvn15zgzOqj9Jk3DO/Ofs/dT27890mKN3LD\ne4gK5cl9+r1ktkz8npEb3sXIWllXXlPr9/uTYuPWmqqKYrIehGHlojSmRBw7QmWhMRqGhyktzETX\nwIKEsBhMnCuolJ/k5Q9OcOfYLfZO38Gw761wamoIQGGqnMjd0GZCU7w6NGT/wg+5uc2KJj3jsW/o\nDMDFn05x+efT9Jo9GDMXS86uPsbm8asYt/sjdPR1Adg+6QdMHMxpMc6YtNijmJu0Y8f7axm7cypp\nMWfQM3YArTiubpfR8o0OaMg0SYs5g41b699tXf5ILu6aRHlJLp3G7EdL24DMB2GIYt2eGn8VhTnx\nHPmuC1o6BjTp+hkmtg1BVJB6P5hLu9//Rxhj/kw6jNqFqW21oUBT9vcogfk4VZXlKkPgi3L33Erc\n/YcjaFQbUisrSrHz7ISDdw+uHpxeo09RXhLH1/TB1W8AL/VZSkVpHqH7PuLCzom0HvITAK6NB+FQ\nv5tav9D908hOuqYyDigRcW86lOyUm6TFnKlVR7emQ4k8/51kIJCQkJCQ+NsjGQgk/hXItGUYWChr\n0xtaGWPtZY9roCdrh3zFxZ9O0eYt5Ze8WwfCuLLpDFlx6ch0tHDyd6PzlL4YWhkDEB96n41jVjLo\nmzc5s+oIaZHJ9F8yssZ8eSk5bJ6wCntfZ3rOGoSGrG4vBVEUObF0H9d2XWTQ16NxbVGdMX3Dmyuw\ncLNG11BO+K4LCIIGDXv50+G9Xqovu096IhRmFXBozjZiL91Dz9SQ1uO7cOnn09Tv1Ig246tPTkvz\ni9k1ZR3RZ++ib25Am7e64dvTH4AVPb8A4Mfhy4DnP/nXNzNQ/VyQlgeAnqmBau0f52nzAxSk53F8\nyV5iL0QCYO/nQucP+2DmbAmAkWU95Ea2pEaHqAwEN/ZfxNQVLJ0bkRZ7BueGr9Blal/ObnkDXVt3\nLNzsaT3Ox2oYBAAAIABJREFUnrBdewj5YTnDVywBIO2GGaauEPRmJwDs/LMozbDiysYQsjonEXfj\nF2IPdidwVAfqd2oEgNxpAyWnG3L7UDhNBwRSnFNIdkIm3WYMRMckgcR7y2g2ojGXN4aQejeJtOgz\n+Lb7gKsH51CSb0d8aDSOTe3JenAVj5dGqp47LfYcVw/OICf1Ftq6Rrj4DaRJtzmqzdHR1T0wsfJC\nS27C/cs/gqCBW9MhNO32udoG6HFiwrdwZc9kWg76HkefHiRHHuNm8GLyUu+AAOYOTQnotRBjq6dX\nrRAEDWzrtcfcvonqPXgWhTnxHP9eWZZyx1xXQLkZajnwO6WAqCD8yOw6n6Wqspzrx+YSd20bZSU5\nGFvVp3GXT7Hz7FTnnJd3fwBA94mn0dKp/kwaW3nh2uRVNdnykhxCNr5OUuRR5AZWNOo8Hbcmg1X3\nc1JvE7b/YzLiL6KppYuDdw8CXl6Itq6xSiY6bCN3znxLfuZ9tOXG2NXrRMtBqwAoyk0kdN9HpNwP\nBsC2XnsCXv6yxmn1IwqyYgg7MJ3MxFAqygoxsqyHX6fpOHh3B5Tvf1FuAlcPzeDqoRkADJ+fD0BG\n/CXCj3xG1oOraMtNcPDuQZNus9HWrfn79zg6embIDa1rtCuqKrj0y7ukxpyhtCANPWN76jUbhXfr\ndxAEofr5QzcQcfZbCjKj0ZYbY1+/G4H9l7P7S18ATm8YCoCBmavq9DzywvfcOfstxXlJ6Js40qDd\nZDwCXns4r9LzoFnfr0iOPEbK/WC8AscQf+MX6ge9hXeriaq589Ij2ffVS/ScdB5TW98az1BSkEZa\nTAgv9VYPAfNp/bZyzRJqL436IOIgmjJtXuq9RPVZbNZ3KQe/bU2Trp9hYOaCTEuOTKu6qkpFWSHJ\nkUfxbT9FbaxmfZYCcCt4aZ0GAgefHoQd+JjCnHgMTJ1rlZGQkJCQkPg7IOUgkPjXYuVhi3tLL+6e\nuKlqq6qopPX4rozeOplB34ymOLeI3dM21Oh76pv9tJ3YnXG/fISdr3p5tsyYNNaP+haPIG96zRn8\nVOOAolLB/llbuLk/lKGrx6sZBx5x+9BVBE0NXv9pEl2mvcLlTWeIOHKtzjH3z9xMXkoOQ1e9xcBl\no7h1MIz8Wtz+z64+hmc7X0ZvnYx3l8bs/2yrKjxg5IZ3ARi8fAyTjs2q1QjyW3na/BUl5WwcswKZ\njozhP0zg9XXvYGBhyKbxq6goKVeNYe3WWu0Ld3pkLrY+Dli7tiYtOkTVnhZzFuvHTukV5RrIdKt1\nKUqTY+aurp+ZBzy4Ho97wGtkJ6RQlFmAa6Dy/cnLiCIr6SL2fg4kXY8DQG6ij7mrNbcOhGJs2QRB\nQ4fzP+5CW08HM1dtivOTcWs6FC25HHMXYxLCokmPu4CiqgIb9zYAFOclc/LH/pjZ+dHznbO06Pc/\n4q7v4NqRz9R0i722HQ0NTbqOP0az3ou5e24FcTd21rrOd8+tIHTvVNqN2IajTw8AKiuK8Q6aQLeJ\nJ+k85iBausacWjeIqsryWsd4hINPD6IurSXrwdWnyj2OnrEDbYYpf4d6vXeZ/tOjCHh54XM/y4Ud\nb5Eee5agwWvo9d5F3JsOJXj9q+Sk3KwxF0BZcTbJUcfxajFGzTjwCG25idr1zRMLcfDpQa9J53Bu\n1I+LOydSlJsIQGV5ESfXvoJMR59uE0/RdvgmMuIvcWFH9Qb13qW1XNr9Hm7+w+n57gXaj9yBsY0P\nAKJCQfD6IZQUptN5zH46j9lPSX4Kp38eWme8eWV5EXZenek4eg+9Jp3Dybc3IRuHk5d+D4C2wzeg\nZ2xPww4f0X96FP2nRwFKQ8aJtX1x8O5Bz0nnaDtsAznJN7i4c2Kt8zwPoqIKPRMHWg/5iZc/uIJf\n50+4eXIhMVc3qWQiL6zm8p4P8Ah4nV7vXqD9iO0YWyoNTd0mBgMQOGAl/adH0W3CSQDib/5C2IFp\neLd6h17vXsSzxRgu/TKJpMijavPfOD4PB5+e9HrvAp4t3sQ94DWiwzaqydwP/Rkz+ya1GgcA0uPO\nI9PWV+n0vCiqytDQ1FYzumnKlMaA9PiLtfaJu7GTqspS3P2HvdBcAIZmrujomZEWc/aF+0pISEhI\nSPyZSAYCiX81Fm7W5CZlqa79+jbHo7U3pg7m2Pk60W16fxLDY8hPy1Xr13pcV9wCvTB1MFc7NU+6\nGc/Po/9H0wEt6fRhH7VTttq4sfcytw5eZdiq8dg1qL0OvIWrNW0ndMPc2RKfLo1xDnAn7nJUrbJZ\ncenEnI+k+4yBOPi5YO1lz8uzB1NRWnPj59vTH9+e/pg5WdB2Qjc0ZBokXo0BlKf+AHJjfQwsjP6Q\nMIanzR9xJBxRhF6zB2PlaYeFqzXdZwykoqSMqDPV7uE2bm3ITLhMVWUZVRWllOZpYO3hg5VrEKkP\nDQd56fcoKUjFxr0tAKFbz1JeJMP2MQ/gimIZD8OKVWjrQ1FWPvrG9phZKo0L+mbKkIToh5sSEztb\nCrOUp7eCIDD0u3Fk3E9lWYcviD/0Gjd+SeDV/42hMO8q5g5NkWnrYe0ahKZuEXkpOaTFnMHAzBV9\nE2Weh3sXf0DPyIZmfZZibOWFg3d3mnSbTeSF1VSWF6t0M7bywq/zDIws6+HcqB/Wbm1IjT5dY42v\nHf2cW8FL6DRmH9auQap2J98+OPn2wcjCA1NbX1oOWEFRTjxZD8LqfL9So08TfvgzGnacRvDPQ0h9\nzACTcj+YjdNNqKwoqdFPQ0MTbT1TAHQNLJEbWqudvj/tWQqyYoi7sYPWQ9dh7RqEoZkrXi3HYefV\nhahLa2vVsyArBkQRo2d4QzzCtclg3JoMxtDCHb/OMxA0ZKTFngOUxovK8mKCBq3G1KYB1m6taN7v\nGxJv76UgMxqAWye/xDtoAj6t38bYsh7m9k1o0Obdh2sWTG7qLVoNXoO5Q1PMHZoSNHgN2cnXSI0O\nrlUfU9uGeDYfjalNAwwt3GnYfgpmdn4k3FImU9TRM0MQNNHSMURuaK06+Y8I+RrnRv3waf0ORhYe\nWDi9RLO+X5Fwaw+lhRlPXYOjq7uzZZat6pUeex4ATS1d/DpNx8LRHwNTZ1z8BuDRbCRx13cASu+n\nm6cW4d36bbxbTcTIsh7mDk1VuRF09S0A0NY1Rm5orco/ERHyDW5Nh+EVOAYjy3p4t5qIc6P+3D69\nTE0vF7+BeAS8hqGZKwamzngEvEZe+h2VgUpRVUls+BY8Xnq9zmcryklE18CyTu+aurBxb0dJQSoR\nId9QVVlOWXG2ylBXUpBWa5/7l3/EwbsnugaWLzTXI+RGthTlJPyqvhISEhISEn8WUoiBxL8aERCo\n3sSn3nnAmVVHSYtMpjS/WHXKl5+So4qjB7D1cagxVmF6HpvHr6LV2M60GNH+ueZ3aOxKelQKwcsP\n0W/RCGTaNX/lLD3VM5wbWhpTnF1Y63hZsekIGoKafkY2phhY1nQxtqpXPa6GTBM9EwOK6hj3j+Bp\n86fceUBucjaLg9RjgytKK8hNrDbo2Li3oaqyVOkmLIqg0MTA3A4rFy8Ks2NV7sWaWnpYOAZw9/gN\nTi7bj1unZOQmz5980d6nJ7cIo6qyFIWiitjwLTTsMJV7h6tlRFHk8LydyI31eW3tRGLD13Pr4C12\nfbiOhoNTsHZrBSi9Hm6XXqCitILU6BCV9wBAXkYkFo4vqW1mrJxboKgqpyArRnVKavLEaameoQ2l\nReqbwLvnV1JRVkj3iacwsvBQu1eQFcP1Y3PJTAylrCgLUVQgioqHJ+e1J8cMPzyLes1G4dP6Hczs\nGnF6wzBa9PsW54Z9yU29rTSAPOZu/bw87Vmyk6+DKLLvq2ZqMlWVZWrrpsYLZoI3salOVqmhKUNX\n34LSwkxA6b5uYtsALR1DlYylU3MEQYPc9Ei0dI0ozk9WGZ+eJC89ErmRrZrLuKGZK3qGtuSl3cXW\no+bficryIm6cWEDS3cOU5KehUFRQVVmKiU3tJ+SPyE66RkFWDPE3qiuaPPr7VZAV89RNa6tX12Bi\n7a26lj+WdDLywmqiwzZSlJtAVUUpiqoKDM2VoSIlBamUFqRh497uqbo9SX7GPTxbvKnWZuUSyPVj\nX6i1PQpleYSesR12np2JDv0Zc4emJEUeoaKsABe/AdRFZUUJmo+7Cz0npra+BPZfwdVDMwg/MgtB\n0MS71cSHBpqaxoaclJtkPbiKX+dPX3iuR2jKdKmqLP3V/SUkJCQkJP4MJAOBxL+azJg0TBzMACgv\nKWPzhNW4Nq9H77lD0DMzoCS3iJ/fWE5VpXoiQi15zWRZchN9jO1MiThyDb9XmiM3evapu4WbNZ2n\n9mXTuO/Y+cGP9F86qoaRQPPJEAWB36Uc1pOhD4LAn5pw7mnziwoRa087+i54rUa/x70ZDMxc0Ddx\nUoYZiCJaegKlBSXItPUxs2tMWswZ0mLOYuXSgsiTEeybuZmX5wwhMfZjtTG19CopL9JSaysvAn1z\npWHFtUlHIIzoKwewLbSnvDQPl8YDCd+4HYOHMnGXo4gKieCD05+jayhHy6gDCfeWkHezIfdPJ9Fr\npjL23cq1FcW5R9H1kZGdfJ36Qc9Z1eExbxQNDVnNewr1987SuQUp904Qd30HjTpOU7t3at0g9Izt\naf7K1+gZ2SFoaLLvq2YoqirqnD4n9TZegeMAsHFvS9Cr3xOy8TVKC9O5f+Wn53+OJ3jas4iiAgSB\n7hODa8hp1mGMMLRwB0EgPz0SGrz87Pk1tZ5oEeA5fg+e5R30HAPU2hx2cAbJ947j32MuhubuyLTk\nnNs+DkXV08M/RFGBx0uv4x1UM6RA/owqE3rGdsp1e4LY8K1cPTiDpj2+wMLpJbR1DLl7/rsaoQC/\nF0+uqexJtx7A46URXNj+Fv495xMd+jNOvn3UPFKeRFffnPKS3DrvPw23pkNwazqEkoI0ZNr6iKKC\n2yHLMDBzqSEbdflH9E2dsa3X4VfNBcp8GDoPvS4kJCQkJCT+rkghBhL/SHaHJxG04CSu0w5w8GYq\nqXk1T2XS76cQc/6uKulcVmw6JblFtHunB07+7li4Wr/QibqmtoyBy0ajayhn8/hVlBbUdLeuDSsP\nW4Z/P4HUyCR2vL+WyrK6N2nPwtzVClEhknLngaotPy2Xwoz8FxrnkVFCofhrMtTbeDuQk5jJ+l1H\nGfrePMycLFSvIzE5qvc2aMFJykz8HxoCzmDuaszSGQlcOpyAtVtrUqNDSIs9Q1X+S+z7dBO9Zg+u\ntUKEvnUJ2dHqbdnR4OCnPPk1dbJCx1Ag4mgI0aE/49jgZTQEPRLDY7D3cwHg9OmriKICQRA4vSuG\nqb0f8MWRTlSWFVBRVoSlU3MAjC3rUVFgSpXGHURFpdpJuLGlF5mJV1SVIEAZ76yhqY2hmesLraG5\nfRM6vPELd84u58y2xQzz3EzMzSzKirLIz7iHb7vJ2Hq0x9jKi4qyQk4dmM3Z/XWPp2dkS3rcedW1\nQ/1uBA1aTei+qVRVluPuP7xGn9O7Ynij8XY0NZUGNVHx7IofyZlVzNsC3i8NZfiHh0EUuXvnDqPe\nX0Oz7p/S87UlGFq4o2dsx6ppF9n1v1uqvhUVlfQc/BmF8o5EXlhNRVnN398X2SwaW3mRmxpBRVmB\nqi0j4RKiqMDY0hNdA0v0jOxqDe8IP5XEmjkWFOelUpgTD8D8pev55LOvKC5Iwdiqfq1zZsRdwK3J\nYJx8+2Bq64uesT2F2bFqMhqaWoii+lqa2fmRm3YXQwv3Gq9f49kBys+epXNzvALHYG7fGEMLd2UI\nx0PkhjboGljVGS4haGgiaGjW0NXI0pOM+Evqc8VdqHNNHsfeqyuaWrrcu/QDSZFHcQ+oaUR8HFO7\nRpQUplFWUnf51WchN7RGS8eA+Bs7kWnp1/AYqSwvJvbadjwCXvvVhqPK8mIKc+KfqH4gISEhISHx\n90PyIJD4x7E7PImPd92k5GH5weLySqKSi9kRfI+uPtYU5xQRdzmK82uPY+PtQPPX2wFKV3xNbRmh\nW87h/2oQWbFphKw4/JSZaqKlq8XAr0ez7d0f2Dx+FUO+G4eu4bO/nFu4WTP8h4lsHLOCbe+uYeCy\n0WjpPnmy+WzMXaxwa+nF4S920G36AGQ6Mk58tQ8tXe26DixrRd/MAJmuFjHnIzG2M0OmLUPXUM6U\nT//Hrr01N0P7tn6JT/0X28A+Dd/uTbm0Phj59SyWTBpFblIWa2Zd5WqI0vXbBbDXFigwK2aXnyu9\njfajIQjUazOO4sI4mnawJyM2iHc/nEtuQivaCllcFnPZ8/m3+O5wo4WdDP1qr3GsG2UTuduI82tP\n4Nnel2Mb2lOQYcCnnyk374Ig4P9qCy78dJryirO0Gv45+2ZuQVuuQ4PuSjdouYMpCk1NVo/+Dpc2\nbUAEf8woyqrEu40BMm2l50NucjaVJbrkF2zF2NZLLXu8Z4s3uXNuBZf3fED9oLcoyI4l/PAsPt3X\nnjZvFeJg/2K5ICwc/TEJ+Jpx0/bQBHteGfoxomEphrLWnE3/jvfek2OqX8bVQzNo2SmGZp3n1zlW\ng3aT2fhzIik53+BgH8SySfce3lkJwPbvd6jJj13QXPWzvokj9zMtGP7mLKISCqmsqsLV2Y6GVjL6\ndlL3htkRXIy2DI7tXoZcrsOdYyLzF61EZliPX9ZNRiaWEBHyDYUFToQer+Tr4D4AnDt9n90/XsMq\ntRHh+1pTmFtJTtZw0kz9CYvIJTI6hdLSCmb1TCYrdwGRYRkUFywjMbmcgGHhrNqwg3tRCWgo6tHu\nXhpWa04Se0ufyvLPuBexnEaD6rNm6wnuRyejpdWV4OS92JbWJylyJlUbSnBYuZ7+k3w4fOU8+45e\nJje/igal7SnRHYzl1jcJ6LWQgV3d6DNiL179mtYZlmBo4UFixH4cfHqioanFzRMLqKooU5MxMHUi\nPfY8ro1fRUOmg66+OQ3avs/hlR259Mt71Gs+Ci1tA/Iy7vHg7mFavPL1c31ensTIwp24a9tJvncc\nAzMXYq9tIyP+EjoPcwkIgoBv+w8JPzQTXX0L7Ly6UFleRGr0GXxav40gCOgbO5J6PxhL5xZoyLTR\nkZvi0+Zdzm0djZldI2w82pF09wjxN3bS7vWtz9RJQ1OGu/9wwg9/hr6JI9aurZ4qb27fBB25KRlx\nF1WVIECZR6CkIE2VSyIv7S5lRVnomzqiI1fmzLh7/jssnVsg09YnJeoE4Ydm0rTHFzWqQsTf/IXK\n8sJajWQABZnRVJQXUVKQiqKqguzkGwCYWHurPFgyEi4h0zbA0qlZrWNISEhISEj8XZAMBBL/OBYd\niVQZBx5hm5nNvfdXEaWpga6hLpbutrQe15Um/VugqaX8mOubGfDynMEE/+8QYdvOYVXPlk6Te7Nl\n4vcvNL+WrhaDvn7zhY0E5s6WDP9hIpvGrWTbpB8Y9PXoF5r3Eb1mD+bg59vZMGYF+mYGtHmrK7lJ\n2ci0n9/goCHTpMuUvpz9/hhnVx/FsYkbw3+YgCyzhNEaTvRcPBy7hyfnAKYmTy+jBrBl/RXCLipP\nUk2BXZuvUj+zis496mP0RBJETR0ZQ1ePJ2T5YY7N2UFZYQkpVR5o6msTFmhEubYW2iUiLrdKqTrv\nCV0q0DW2x39QV86vmUNuYgaWLoEoFJU00dZFo1KghWAKhcCVbFKoR4kzBA1RzmdgU4LvALix7woh\nK49QVmGHoS3YN6yOHW87oT9x1/aRfiuIvdPOYOfrxOCVY9HRV8Y3a+prEyzL4Q1Tdy6uO4lYYYOV\nthzLpkdxbV5dWi/iUDjWPgagmYa1W28Aju6/zbGDdwAQhP9x42QpOqd2Y6wbRYuWg4Dak1I+D4+X\nLpw5th7+fYZzIeQga9fv5pUR8xnbJZNX35hJ3sbX0Nape5xK3S7I9M6hU7KcqLNf0m2gKRX6r5JX\n4c+d4FzKCwXeX+aCs6fyBFTPUIsLBxMQFSKLFl4iV5iLi2kVDSzC8fMtpsyiK/O+jONBXhbm3mkc\n2Xeb1OR8bIy7Y2cai62NBZqaGrQcsBJxy1QcDOxYt/waCuToy/UgXaBZF0f0DJSf62aBLvy89BIe\njSzp3KsR3029iJl9Y0LDTmIr5mLuakhwhB1Xzk7CtYHI9PUdOPB1CxJSZ7B80gXavtOQxXPfZvOC\nodw46UmlVzKzt/cmPT6SH2Ze4LsZUdg0uMqosT44NJ7IslG3uCW/x/Kdo4i/uZt9399n8UQFydbH\nGdXTih7D5nF0QxTXj5uio5/Lse97AdDApRVxwsA6T5r9e87j4s63ObqqGzpyE+oHTaCqQt0DqlHn\nT7j0y3vsXuyHorKM4fPzMbX1pcvYQ1w7+jnHVvdAoajC0MwFxwb/Z+++o6MsvgaOf3c3vffeEwgk\nJBBIKKH33qUoAgpS7CIgYKEqXVDpTRAQaYYmvRM6gQABQiCBhBTSe9/2/rGysBKqvqI/53MO55hn\nZ+a5uymeuc/MnS4v/bNTveEw8tKvE/nL20iQ4BHUnRqN3+Xu5c3aNjXCRyLTMyT25CIu7f0KA2Nr\nnYl4vc7TubT3S25f+AlTK3d6jL2CZ1APyoszuRH5A1G/jcPUyp0GPb7H1b/dc8XlGzaQa8fmPtcT\ne6lMH596b3L38maduOLOLOPa0bnar4+s7glAeN/l2mMus++d5+qhGSgqS7B08Kdh74V41+n72D3i\nL6zBxb8dJk/YynF667tkPXLywZ4FmqRGz/Gx2uMuE69sxbtOX2T6L14vQRAEQRD+TpK/Yq/zP0Vo\naKg6KirqVYch/D/zHr+bqn5qJcDdmZ3/7nBeudK8Yn5oN5UeM97Ubqd4WV+8NQPDGzl8dnhalUkP\ntVrN8jU72bj1IBlZuXi6OzHi7R706NKMjWsvkJlRwNJNPzHl83fYu/cCJmpHnFzNqFHXiikzVvHD\nnE+ZNX89dxJT2bVpDvsOnWXvwbPsi5jH0nFn2RF5hzijs5CfDaixknlRsyCYs93NSZijmQgNqP4L\nTZpIePfH/oz9aiE5dytwLg4kNb4AF18L/NuacOCHNMauakqdpm4kp2TQovOHbNswg+BAzT7spePO\nsnPHaT5c0IiObTVF+9Izcvhg+EBuZVigLzUj0DAMoxJblHI1XgHWODVS8cP6tcScXc/xiDusmnie\neIPzNLJrRU5aCX4hdtToYEDMd9s5qs5GYm9KI48mVMSbkZ1Wip6xhCyTeFr1rUZ6eh4JRyqxkNhR\no501P+3+ma+Hf8qvc28yck4DjsQeY9+hszikBiM3L6TFGx4M7deTNVMvEheVhbxCia2zCb0/DELi\nWMyQIbMJqejAtF/b4RNki0qlZs2UCxyKiCXTKYb9+2bxaZvdhHVxIvLeUS7H3KakpBwfLxc+ea8f\nrZrXY/kPJ8jMy+DklTPsi9Cc6x555DbySiVrZpxCVmbKt3u7YmP7cN/4vvU3Wf9NNHKPTEzy7FCX\n6WNsI6N2F3tGjm3GgSPnGf/FChq7t4VsA7JTilFK5Zg4y8gzv0erjtX5YekW/N1qIpPKSM9Lp3+f\nljSoXZel712g+4c16POuZgXH1esJTPx8I906NKdPvzBGNozgi3WtCGigWaGx9+AZvhi1ngB5E5ad\n742ppWbbw6w5G7iyAj5f3YpajZ04sjGe9bMuclZvB+ePrcLM1JgNa46ze3oac/Z1xMXHijsxOXzV\n+wDRhvs4eWIBNtYW7N5xgQ1j4xm7pjF1wjUnkmSnlfBxi518e7ALTp6aJSsRu47z7Q8bOHVw2Z/6\nXfwvy0w8y8EVnej52bUnTsofVVaUwa759en0YSRm1lWfFvMqaeILo9OHJ/+R8QmC8L9HIpFcVKvV\noa86DuHfSdQgEP51XKyqflr/pOv/axLP3+bW0WvkpeSQejWJbePXYWJlik/jFzsHvCr6maXkepo+\ncUXEtwt/Ycu2w0z+fCj7I+bz7tCefDltOUdPaI7Pk8mkVMjLWbBiE336Nia8iR8VxZongBWVchYt\n38rEcUOZMPwDNiy/TOp1Cd42gSQn5QIgLcpFZmRGaMPudArtSYhxbaRmEKKv+6cqp6iSyWN3osqw\npzLaGUtHQ77e1oEeHwVw5EfNGff3UwuZO+0AC2ZE4uvsx89LLlNSrLuUO+F6Ad9+fZC87DT6vvkp\nJgYO9G39OsGKNuhVWFDgmMxXv7SkRpgDJ5flEubZlG2bogFQKdT4UY+Qrg6ED3Llzq0sts+6i1u7\nINZtm8WwLgO4d1SN1KOQbmOr41DTCItMLyJ+Oo9vNSdGjGkJJVJKiiuo5RXMncv5mFsbsmPDRW5d\nzadlYAfMVLbUCw3E3tKB1ZOjqCxT8MXaVgybF4qRp5pNP1/g+J5EnGw0J0YUFpSjkKtY8Mkpzh+8\nh1sTM4J9w/j6i92Ul8mprFTQvHEIa5d9xW9b5tC+TQPe+3QO164nEh+XSbce9bmTmMqVGM2Khqat\nquFVw5zsvNwqfx7upxSgVkFJErw7uxFTt7TFzNSI87+mkZ1ZTLtW9QmwrU1ejILX3g9m4uaWFLvd\ngUI9nMurM7BfR84eXo5cr4BGLbzZs306I4d1xc3BBrUCcksf1hiIuhSLSr+MW7GZVMoVVcYjQQoS\nCfqGD39e5CpNzY+4i5qTE+SVKmR6Uioq5Vy7odlvHxjgBcC6FZEolUosHPWRGqqoZROCuakp8kol\ne9beAEM5B86epHHbEbTq+iGL1m7CwtaQm+cztferXcuP9MxckpLTq4xReDKlooKS/BSuHvoGj1rd\nnys5AJoaAo16L6I0P+XZjV+B4rwkGvT8XiQHBEEQhH8FkSAQ/nXGtvfHWF+3Qr6xvoyx7f/8BPnf\nQKVQcnzxXlb2mcvmj1ehb2TAm6vex8D4KevHn1NJPUf2JNwkqOGb2n9vv6c5mqy0tJwf1/3GjMnv\n0rxxCO5ujnTr1JR+vVuzbtN+nXEG9e9I/ZAg0pKL0Pv9e6VUqpg4fgjRJzNRKSUMfb8Jjn5qSisL\nWfapA2TKAAAgAElEQVT9CeSVCiwrrAm7Uwv9oyryjikpLICUBiZ0qKc51vHBiieZqxND3m9CWWEx\nKqWKNGU6Pd7+lNc/HUOJtWaydvHcPXq/UZeBI0JJykzE0EjG1Uu6E4iMlDLq1vdg2iet0VODk0Nn\n3OycoELK8Jn1cXFxZv/RS/T5JBhTGxnK7IfbONQqMPFRcfNuGtXq2FPhm4i6Qp9GnTrg6e5EfGQp\n1cItORC7DwsHQ+xrGFNqnoW3LJARb3enWUd/lAo1ns72+HtUI+laAZ2H1iDrthxHS2cah/ljYCij\nVkNX4q+WkJlcTPV69lg4GrJz52Xqt/Pg8+/a4xtgQ3U3zRF2FWUKvh15nPhrWfi0NafHwEAOXNqD\nb5A5UqkERwdr3ujbDv9qnnh5OPP+sN4E1vRhz+4oZHpSagV50Cy8Dlu2H9W+zy3bjmJhYVblz4tS\nqQY1ZFjepH4LHzxqWNP6DV8q8tTciddMyGWZVhi4KWje24eadVxRWZTg0lBGcbKS3KxS7O2skclk\nmJoYYW9njamJMdmppQBYOzxcjp16PwszCxkqpUqbUPqjYmkuRiZ6bJh1mfJSBeWlCmSJjkiQcDnq\nDgqFEsfqhpQUynGRV+d+ei4lBZUcXadJKh09eJmaYW/QsN07FPnE4aLnzdvBW3g7eAv58RKu60cS\ndyeRRd+OYfL4oZw4dZkSeRFZqSXaGBzsNfvbU9KyqoxReLI70RvZPrsWlWUF1Ov0zbM7PMI9sAsO\n3uH/T5H9OfYe9fEM6vmqwxAEQRCE5yJqEAj/Oj1CNHs65+yPIy2/DBcrY8a299de/1/nE14Dn/Bn\nVwN/WWF1a/LNxBHar40MNUu14++kUFEh5+13v9EpiKhQKHF1sYcGkJSQS7eGvbgdVcnMc5oCkF17\nB5NZnIqengxDiRlpKflMntUNfQMZeoaQUZxM7Rq1yIktwcJdSpF5DgqZlBtlpXjJ3fG5pKDnRM3W\ngIRbmklX8zbV8fCyQVEsQWJWgZObC5+3G4lfgDWrF58gMxmC6jrh7WtHSqoKhVKOVzVroi/co1Ez\nzVgGegaUlyoJCXPnWNRoHCoLyC7MZu2aGJzKavDdO6eRSqRkncjh3LItVJYrkNo9klOVgMxcjY+f\nPS3b+fPhV9eoiQMfvL+QHKNEAvLakmYcS6m6gtJSzR7zCqMCLAo1S+KNTPXxCrShNFtJca6c0iJo\n3seHjfOucCspjvjoZBysXbD3ALN4I8xCTNmx5DondiQgM5cQVMMdB0dz3LwsSMm6hyV+rJ1yCRtH\nE2r3sMPC2hgHJzPKKkqxsTfCwFCPykoFM+ev4+iJi2Rm5aNQKKiolOPnWh0LcwekUgn9erdh7FcL\n+XLsYPT19di++wRNXNuTfffx0zccnMxBCq4uLigUKiorFcTGpYEacu5rJs2KIimKQhlvBW1CKpNi\nU16Xe7Eq1Eq4n1SEn7/jY+PevZ0NEmjY9OHRfOXllRgY6GNkrE9BbtUniCgklXz0Q2NWT4ri0Ibb\nSKQSGnX2xNypgAuXbxJQfwMG+vr07dID+S4/fh2Twna9NJr28UApqyQwwJtvJ7xJQX4JCz84T6bx\nfaZu6o1aLWHK+9vwzQlj1sR+2Dlojt2bPGEoc4ZEUpBX+tjvS0X5048tFB5XLWww1cIGv+owBEEQ\nBOE/TSQIhH+lHiGu/5mEwN/N2MgQLw/nx66rVJqn98t/GIeLs+5Z3vp6ekQeSsTVw5JVERtYMGc0\neekKcrKKadKyGhG7UjHQ1+N+agHySiWTx+0EQC6XEODQgIy0QqTlRvj6uhDWvzZ7Im7goqogKS0B\neZo3RzYl0OeTYFLuaY4y27w+it2Ho6HMBAOpmrzsUlQKKcG1/OjQtpK1h64QfS2W7tRBKtVkM7yr\nW7N36y3yckpQqVQYGRhjZWeAlbUJKpUKeys7mjTzpyKoLqciUhg+vw73U4o4vPs2n0xow9GTUZw4\neEfnfSMBN0/NE2OVWoWZmTE9B3UnvKcbX/c8ycfD+xLa0Zmbl3MednkkxxBQ34HLp1KpUKvxq2NH\ncUk5BhYShvfpzZFNt1FYFPLZpMU0rBlOaEgA8490Y8m0SPKTK5jc7yDdRgTg2khKfnEelkBguCMX\n9qXQsJcbpy7cIvZGKrW8aqMv0WwZOXIiiqv5F5gwehBeHs4YGRkw5suFqJRq9I01gbVsWhdjI0P2\nHTqHuZkJhUUluNayI/vu/cd+JiytjZFKwd3Wi88/jkAqk1LN3Vn72QCoVSpM/OTYuVkil6tQKPSw\ncJBQnC3V1gl41N2EbM6eSQA1ODo+LI5pbW3OjZt30beWIZc/+TjF4CbOzD/claLcCqR6EkwtDHgv\nPJ33P+xK/e7DsLQwJSUti/aHRrF+0VRCQvxYsHwrUqU+fV9vQmBND45tScBMasHhwk3kK5sTWrcm\njs0qSIuw4uaZXJp01yQIfH1c0VPro5I9TJ4UFGq2RdhYP7uwpyAIgiAIwj+N2GIgCP9x26NTaTzz\nCN7jd7MnJp30wvIq2/n5umFgoE/a/Wy8PJx1/rm62AOgpy+jpLwYa1tjevStg7xSyaE9N7RjqNVq\nzMyNGDWhLaMmtKXYsohj8afZbWJAbHEFGYXltGgVwKSZXXlzcBP8fDxRqeDqxVRtf4AuPYMZNaEt\nevYlVBar+HhcGzp0DQQg867mybVcrpm0PZioKSUV2DuaE30hmfz8YowMjHFy15yuEFjTh0q5AiMj\nQ0Iae1CSV4m7qyPVg5zRM5Hg6GmGmZ0eEv1HymOqQVkkwcBAs4Ui0MefymI1QaFueHk441bNitzE\nSrw8nLVJCsNyS0wfWYVQs4EDabeLUORDrXAnAAysJcRH5ZGXLOfDzzqzcsEEsrLzKSsrx9bJBLdg\nc+r1cuK1j4M4sjlB53sU3s2TgZ/XZeus63TvFEJiZgI2llYc232XkuIKUlKz6Nm1OR3aNKRGdU+c\nHW25l5wBEhWlpZrPS09PRu9uLdiy/Qhbth+hfasG6Onpbul5lJ6+jIPRuzF1K2bK7G6ENdKcDGFl\nY8r+w+coluTjZG3LjKU9mTivM7eKo7B0laBnIsHeUXfrwt34bFYtOkn71zRbJlLjC7SvBdbw5k5S\nGqWllZiYPns7jbmNIaYWBlw/k05hTjn1Wrvh6GCDkZEhu/aexNnJlvqNqmNkqk/mdQVIVdRqrPke\nVJQptdXzVb//zNWtUx21Wk35IysDbt9OxVBtRkDYw4RaXHwy+noy/KuJ/eaCIAiCIPz7iBUEgvAf\ntj06lQkRMdpjI0srFdxML2V7dOpjKzTMTI15Z3BXZsxbi1qtJqxeTXZfusfKXefJL5PTzNUPHzPd\noxbbdq7JykUnCQrXPHF1dbemuKgciRQikws4eCsdZVkpKqmU4koFN+PuMWPZYXqEVyc/p4CE01kY\nKR0oVBShkCtxddc8rUcCdg5mGHuVURBjwMZZ0bQd7EtaUj6HN9wGJNSto6lJYWRkSEhwNZat3sFr\n7bpwOvI2cTczkClN0VfLSLyRR4hPEPuLE9i/8wqDPtTHs5YlX799EH2PMoxMTUiOLSB2fxGqwocT\nZYkUShOkZNwpJvFGHl6ldYiX3Odg9DEMHBoT2tWBjdNucDszngZNAsi6WYZJkT3uHR5+Rs7VzFEq\n1JRmKAls6IidvRlGNhIuHU5FT1+KzLqSHVsisbO0J/OynCsn0jDWM+Tm5XRkOUW4+mqSH5am1pCt\neXrtE25B3Z7WfP/RKe6a3uP75cMxUJsz78hJbJwtOHDkPG1ahqGvJ+OHpVuoqKwEPTklBRWUFFdg\namZI316tWbZ6O1KplDVLvuTi5vwn/gxJJBImjB7MtNmrMTSSYafyAuBGfBwL1vxCwwb1uXehlF9/\niKFBRw8kFQZkxKipyJLg6mGtHaeiBFYtOknbLgE0b12d42vuEXcxC58gWwAahtXCQGVCWa4SKjWT\n94ykYkoqSlDry7V7/n9ZeA5bN2Oq1XQh9WYR6765hGuYHkXKPG7FF7D/8Dkill7h/U8HkHmvhJhT\n6WSe1SdJdpkfNxjSpWMTjJzlFBeWE2jaEFtDO1JuF5Bz1hiJBCJO7MGjnmZlxZxpEVjKqhPWwkv7\nPqIuxRJatybGf0FNEEEQBEEQhL+bSBAIwn/YnP1x2uTAAyqVmjn746rcwvHp+/2xs7Fi5dqdfPH1\ncpRSfdTm1qh9giitVJKQpXtKgG91BxydLEi9o9mjXa2GA14+tqxZepojchUGyDA2scShXI5UDZYl\nZlz7NpNr32aikiixsLbDv4s9mCvQ05dRrYYDAMcOxuEWaI5UKuOe2VWUxxVcOJBKhawYUz85FNpS\nu/bD/eszp7zHhClLmb10JS1rtcfc2JKCDAX756azf+6+31tZIHWQ8O3sXSTeT8JHHYxBlDUoyvnh\n6imMHVVg8HAFgVRPgpGHimNrk9jzQzx+dWwZOqMe63bsZNW639CTyfBzq0XedU+2n4pD30RKoUMi\nVl61SE8rIPFODkf23cTERoa6QopXgDVSmRSXAEOyo0soluQz6J1vqF2tNlamVrhYWPLTtIvk3C9F\npVbhVhPenhxGTGwC7vYeFCXBmC8XUmGYT51qdbGva49BTCPKUw25lZmKVCahdYtQ9t7MpP9bE/Gt\nrIetnhthDeQgU2Bmbsjd+Gxq1XHFw82R+vUCyMwoxMPJlZNlmsl3xv1CykrlWNuYYGKq2R6gVKpo\n3aQhNpbW7Nx+npRCzf9WIn47ysfv9mXw6x3ZsPAil4+n8dvKWKzlgWSlq2jRx127ssLUwILsuxKa\ntfYhJNSDwoJywrt5Ern9Lh3f0tTbsLYyp55bfbLOKVh0TnPm/MovzwOQohdLin4sAMsW78Ve4YmR\n7AYO7mZ0HxnIz6c20u/t36islFOzuhet6jbh2JJM9s3di4uPBe983YB8Q3dW/LSD5Wt2YGRkQFBI\nMO5lNZg5+ARIwLOmNSPm1WP1zl/pOWA8luam1DZqQp0ePhgaP/xf6a69J/n43X7P9fsnCIIgCILw\nTyN5sGT3f0FoaKg6KirqVYchCP8a3uN3U9VfAAlwd2bnp/ZtPPMIqfkPi8WFlVVioFaT6GLFqfGt\ntNejL9xj07ooxk1qj7WtKeXlcvbvvM6B4/EYqaFcIiFbJuWakR4lUik1K+S0tjEhN7sEPX0pnt62\ndOkVjKOz5mn5g/4xl1MpLirHzNwIL19bOnSrhZ29GRsXXWLX93EsPdsLc5vHn+IunneMu/HZvDUy\nnMBg3WPUYqJTObD7OlkZRZiZG9GoqQ+tOtTQLjdfMv8YTi6W9OwXAsD0L/cQ3tyXFm2ffoLGgd+u\nc3CPZgIrkYCRkT72TuYEBDnTuIUfRkYPVxUo5Ep2b4/hclQyZWVyXN2s6NIrGG+/h3UfbsSksevX\nq+TnluLuaU1YuDeb10UxcWYXzC2MOLw3luio5Cd+hgDTBhzCxceCodPqA7Bneww5WcUMHNYIgPY9\nP6FpSBMKqjitr+/AUMIaeQHwy5rz3LyeTkWFAgdHc5q3qU69Bp467Zd+d5zU5HwUCiUurla07VyT\nGoEPl+VvXHuBi2eTdPqolWqyzykZv6IVNcI0iaHvZh0k8vx51vw0CnMzk6d+5v/fCnLK+azjbqb9\n2h4Hd81WiaMnLjJz/jp2b/n2qdsyBEEQBOH/k0QiuahWq0NfdRzCv5NIEAjCf9gfJ/kPuFoZ60zy\nq/Jnkgt/9t6POhFxBwd3M2ydTUi+XcDaaRdx97di9JJmzz3Gv13kkdsc+O06U7/trk1mPE1pUSVj\n2u9m1u5OmFtrkijFReXMmXqAQSPqc/bSFaZ/u5bIfUteabG92AuZlBRUEtrGjfS0ApZ9f4KaDcwI\nCvSmpr/XK4sLIOFKDpkpxTTq/DAZsnv/aVyd7akTXO0VRiYIgiD814kEgfBniCKFgvA3Cndy5Miu\nXX9qjA2LF9Mr9K/5mz+2vT/G+rpPOo31ZYxt//Qn4gAuVsYvdP2vvPejCnLKWfLZWca0382aKVHU\nbubMe3MasWfTJj7o3euFxvq75GZl0SkggMy0tJfqf+p4PPcSc8nNLiH6wj0O7Y0ltKHXcyUHAEzM\nDVh8uqc2OQBgZm5E34GhvPH2VL5bvImvvxz+yivx1wxzILSNGwAF+WX0HxxG314tX3lyAMC3tq1O\ncgCgc/twkRwQBEEQBOFfTdQgEIS/wNcffcSezZu0X1va2BBYty4fTJqMV7V/3oRh98aNfPPJxwBY\nA9YmFpTbeGPUcgCf9WvzXEdIjm3vr1PgEF5sgv/gHnP2x5GWX4aLlTFj2/u/8PGVXYcF0HVYgM41\neWUly2bOYPKixdprd27eZOWcOdyKuUravXsMGT2Gd8aO1elXUlzMilkzOb5nL3k52XhalND9g6/o\nNVzzWQ0OtmPYN4s5fyaa43v3UpCXh5OrKz0GDaL/iJEAFOblsXLObM6fOEF6SgpWNjY0btOW4ePH\nY2ljA4CNvT0d+vRh5ZzZfD7/uxd6vwD3791n1eh22IZ8hq2LJ42a+tCmU8CzOz5DYLAL5yIXP7vh\nMywcPQTf4Hp0HPz+nx7rAf8Ap79sLEEQBEEQBKFqYgWBIPxFwpo1Y9fVGHZdjeG7jZuoKC9nwpC3\nX3VYT2RkbMyuqzHsvHKVVRFbCPc0w+noAjoH2j9X/x4hrszoFYSrlTESNFsDZvQKeqEJfo8QV06N\nb8XdmZ05Nb7VC/VVqVQolcoqXzv62y4MjYwICQ/XXisvK8PZ3Z3h48fj4qE5gi7xxhXequPAtEGd\nAJj56SjOHTvGVz/8wPqjxwBY/e23ZN2/D8D3R65z4uhpTh86xMQFC/nlRCSDP/6EJd98w94tWwDI\nSk8nKz2d97/6ivVHjzFp4SIunz3LxHdH6sTYuX9/DkREUJiX99zv+QFF5iEadezMnBUjGT+lIx26\n1WLj3C+Z1L8174S6MrpDSJX9zu3fzld9WjCsvjuftq/DntULHmtzZvdWbZuPWgawdMJI8rMzdNqU\nFRexfuYEPm4dyNB6LoztHMa5/du1r3cfMYZdK+ZTWlT4wu9NEARBEARBeHVEgkAQ/iL6BgbYOjhg\n6+CAf3Aw/YePIOn2bSrKHt9n/8Dir6fRv3E4Lbw86RUayqKpU6koL9dps37hQroE1aK1jzdTP/iA\n0pKSx8b57ZdfeKNpU1p4etAvvBEbly1FpVI9NV6JRIKtgwN2jo7UrFOH/iNGkJ6SzL2EeG2b93v2\n5NsJE3T6ff3RR4x5cwCgmeDXObeA99QnGKg6w7IBbegUGMCCyZN17t8rNJTV8+cxa+wY2vj50j2k\nDj8vWqQzbnFhITPHjKZTYABtfH14r0cPYi9f1r6+e+NGWvt4c/rQIQY0b0ZzdzcSb92q8r0diIig\ncdu2OtcCQkL4cPJk2vXqjaGxZhvE8Yj1tO43hNT4WBJvXOXY7t28+8UX1G3cGDdvbwBsHR2I+GkN\nAFZ2jly/eIkOr/WhXpMmOHt40LFvXwLr1uPGpUsA+NasyYwfV9O0fQfcvL0JCQ/n/YkTiTpxgpKi\nIm08vjVrYufoyLE9u5/6ffqjirJSjkesp3nPATrX1WoVTbr1J7xr3yr7XYk8xNLxI2jRexDfREQy\n6IvZ7F+/lIO/rNS2uRV9jmVfvEfjbv2YHnGSj75bS1pCHMvGP0xuKORyZg/vTXrSHd6fs4qZO88y\nbNoC7F09tG3cqwdg7+bJ6d1bXui9CYIgCIIgCK+WSBAIwv+DkuJiDu3cgW/NmtrJaFWMTUz5fP53\n/HIikjEzZ3Jox3Z++v7hkvPDO3awfNZM3hn7GasPHsLDz5eNy5bqjLFj/TqWzZjOO599xoYTkXw4\neTLrFy4kYvXq5463qKCAAxERAMj09J/R+nEHIn5FJtNj2a7fGD19BptXLOfQju06bTYtX45vjZqs\nOXiIN9//gEXTphITdQEAtVrNmDcHkHU/nbnr1rPm0GHqNGrIh6/1Jjvj4dPryooKVs+fx7g5c/n5\nRCRO7u5VxnP1/Hlq1K791JiVCjln9/5Ki96DCGvbjRM7fkGpVGJgaKTTTl/fgKvnNMfpDQ62w8vT\nkZMHD5CRmkpuxn2mD+1FUcIpbh75mXnv9yc9KUHbd9viWXzeswnXzxzG01bJqLa1+P7jgRTl5QBQ\nMySEc4f2Muudnoxo5MXwBp58+VpzYs9HPjHuq5GHkEgkVAtpoHN94ISZtH1jGE6evlX2O/3bZuo0\nb0/r/kNwcPOiTrN2dBn6CXt+/IEHxWrjr1zAxtGFDgPfxd7NE7/aobR5YxgJMZe040Tu2EBRXg6f\n/LCO6nUbYu/qQfW6DfGpVVfnfiEtOnB2b8RTvweCIAiCIAjCP4tIEAjCX+Tc0aO09vGmtY83bf18\nuXzmDJMXL3lqn7c//ZTg+vVx9vAgvE0bBn30MQe3bdO+vmnFcjr27UuPQYPw8PXlrU9GERCiu3x8\nzfz5vPfVRFp17YqLpydN2rVn4IcfErFmzVPvXVZaSmsfb1p5e9HevzqHtm+nSfv2L1Uzwat6dYaN\nG4eHry+tu3enbuPGXIzUneTWb96c14YOxc3bmz7vvIObtzdRv7e5eOokt69dY/rKlQTUrYubtzfD\nx43H1dOTfVsePoVWKpWMnj6D4Pr18fD1xdTM7LFYigoKKC4sxM7x6XvWs+/FYevsjnv1AMK79OH8\nvm0E1qvLT9/NJ+v+fe32hXsJCeRkPkxSdOn/On4BgfSqF8L7rYKJOnmS1oM/ZcrGw1jaOTJ7eG8q\nykq17bPS7nE8YgOBLXvx2bKtJN2MYeuCbwCwc3Ii9WoklvaOTNpwkGlbjtLj3c/Q/0OS4lFxl87i\nFVD7uQsSPqCorETfQPfYR31DI3Iz0shOSwagekgD8rMyiD62D7VaTVFeDuf2bSO4aRttn0tH9lIt\npD7rZozno5YBTOgRzrbFs1DI5Tpj+9Sqy52YS1SWP3kFjSAIgiAIgvDPIooUCsJfpHbDhoyf+y0A\nhfn5RKxZw6j+/VixZy+OrlXvrT+yaxebVywn5e5dykpKUKpUqB7ZV590+zbd3tBdSl4rNJSUu4kA\n5GVnk5GayuzPxjJ33GfaNkqlkmcdYWpkbMxPh4+gUCi4fPYMvyxZwrg5c1/mreMXoFsgz87Ribzs\n7OduE3flKuVlZXQK1G1TWVFBalKi9muZnh7VatV6aiwPtmgYGBk+tV1G/FU6DBgKQI3QxhgaG9Pl\njd7s3PIb3UPqIJPJ8LGF4Pr1Sbn/sE7AmSOHuRYVzZAPhnLtxB7C+37I0unfUKNuA96eOI8PWtTg\n8okDNGjfA7lcTmV5GRaeDRkz53sMjYxo0XsQkTt+AcDQyAi1opxaDVvg4q1JzDh6+Dw17pz7yVjZ\nv3jBvlqNW7Jh9pfEnD5KYMPmZN67w761moKEBdkZ2Lt64Fc7jPdmr2DphJHIK8pRKhQENmrB8K8X\nasfJSkkk9nwkDTv1ZtSiDWSnJrNu+jjKS0t4fcxUbTsrByeUCjl5Wek4unu/cLyCIAiCIAjC308k\nCAThJW2PTtVW4HeOuY+PuUS7bx3APziYdtX82LF+HcPHjX+s/7WLUUwaOYIho8fw0ZSpmFtaErl/\nPwunTH7uGNRqzT7/z2bNJigs7IXil0gexutVrRo5GZlMGjmChREPVzBIpJLHEg0Khe6TYgC9P2xL\nkEgkj9VAqKqNWqUZW6VSYWNvz5IdOx8b2+SRVQIGBgbIZLLH2jzK0toaiURCUX7BkxspKynITKFR\np97aWBp1eo1rJw+yePt2ykpKKCkuZnTbQFRKJS4eD4+zOxARweeLfyTx4lHys9I5uGIa7pYVLBnV\nnx+NjKksLyUzOZHSkhL2//orEj1D5v6yEUMjzaoAKwcnCnOzACjKy8fYzosfp3zCyZ0bCWjQjNC2\nXbXJgqpUVpRjYft8hSQf1aL3IDKTE/n+44EoFXKMTc1pO2A425fMRiLRLCZLTYhj3czxdB8+mlqN\nW1GQlcHGeZNZPXU0I6ZrkgkqtQpzGzuGTJqPVCbDO6AOxQW5bJj9Ff1HT9GubHiwVUP+h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52FNjwveqx6+v3ybu5Wta/LYcAxvltq01Jo7iaJ9hanWrPKRvtjLsqDSgNw+27cZz5CC0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\nx4gpBoLwNzl8L4wpB/0Ji0tBAYTFpTD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f0b6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "start=50; end=100\n",
    "X = fac0[start:end]\n",
    "Y = fac2[start:end]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[start:end], X, Y):\n",
    "    plt.text(x,y,movie_names[movies[i]], color=np.random.rand(3)*0.7, fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Neural net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than creating a special purpose architecture (like our dot-product with bias earlier), it's often both easier and more accurate to use a standard neural network. Let's try it! Here, we simply concatenate the user and movie embeddings into a single vector, which we feed into the neural net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class UserMovieModuleNN(nn.Module):\n",
    "    def __init__(self, num_users, num_movies, num_dimensions):\n",
    "        super().__init__()\n",
    "        self.users_embedding = nn.Embedding(num_users, num_dimensions)\n",
    "        self.movies_embedding = nn.Embedding(num_movies, num_dimensions)\n",
    "        self.predictor = nn.Sequential(\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(2 * num_dimensions, 70),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(0.75),\n",
    "            nn.Linear(70, 1),\n",
    "        )\n",
    "\n",
    "    def forward(self, users, movies):\n",
    "        u = self.users_embedding(users)\n",
    "        m = self.movies_embedding(movies)\n",
    "        \n",
    "        x = torch.cat([u, m], 1)\n",
    "        r = self.predictor(x)\n",
    "        return r\n",
    "    \n",
    "    def init(self):\n",
    "        torch.nn.init.uniform(self.users_embedding.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.uniform(self.movies_embedding.weight, a=-0.05, b=0.05)\n",
    "        torch.nn.init.xavier_uniform(self.predictor[1].weight) # Linear 1\n",
    "        torch.nn.init.constant(self.predictor[1].bias, val=0.0) # Linear 1\n",
    "        torch.nn.init.xavier_uniform(self.predictor[3].weight) # Linear 2\n",
    "        torch.nn.init.constant(self.predictor[3].bias, val=0.0) # Linear 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_nn = UserMovieModuleNN(num_users, num_movies, num_dimensions)\n",
    "if(use_cuda):\n",
    "    model_nn.cuda()\n",
    "trainer_nn = ModuleTrainer(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/1: 1257 batches [00:56, 22.14 batches/s, loss=1.1347, regularizer_loss=0.3168, val_loss=0.8216]   \n"
     ]
    }
   ],
   "source": [
    "trainer_nn.set_optimizer(optim.Adam, lr=1e-2)\n",
    "trainer_nn.set_loss(criterion)\n",
    "\n",
    "regularizers = [L2Regularizer(scale=1e-2, module_filter='*embedding*')]\n",
    "trainer_nn.set_regularizers(regularizers)\n",
    "\n",
    "trainer_nn.fit([tensorl(ratings_train.userId), tensorl(ratings_train.movieId)], tensorf(ratings_train.rating), \n",
    "                batch_size=batch_size,\n",
    "                val_data=([tensorl(ratings_valid.userId), tensorl(ratings_valid.movieId)], tensorf(ratings_valid.rating)),\n",
    "                nb_epoch=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/4: 1257 batches [01:01, 17.46 batches/s, loss=0.8356, regularizer_loss=0.1641, val_loss=0.7972]   \n",
      "Epoch 2/4: 1257 batches [01:01, 20.37 batches/s, loss=0.7552, regularizer_loss=0.1531, val_loss=0.7847]   \n",
      "Epoch 3/4: 1257 batches [01:01, 20.45 batches/s, loss=0.7154, regularizer_loss=0.1740, val_loss=0.7793]   \n",
      "Epoch 4/4: 1257 batches [01:01, 17.77 batches/s, loss=0.6885, regularizer_loss=0.1980, val_loss=0.7770]   \n"
     ]
    }
   ],
   "source": [
    "trainer_nn.set_optimizer(optim.Adam, lr=1e-3)\n",
    "trainer_nn.fit([tensorl(ratings_train.userId), tensorl(ratings_train.movieId)], tensorf(ratings_train.rating), \n",
    "                batch_size=batch_size,\n",
    "                val_data=([tensorl(ratings_valid.userId), tensorl(ratings_valid.movieId)], tensorf(ratings_valid.rating)),\n",
    "                nb_epoch=4, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This improves on our already impressive accuracy even further!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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